{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"12xgboost.ipynb","version":"0.3.2","provenance":[],"collapsed_sections":["b2hNKazZt5Wr","fPAUBD9dzPqP"],"toc_visible":true},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"}},"cells":[{"metadata":{"id":"gT5FNpCBh7OQ","colab_type":"text"},"cell_type":"markdown","source":["# 2"]},{"metadata":{"id":"q13BZqpetttk","colab_type":"text"},"cell_type":"markdown","source":["##  提升集成算法：重要参数n_estimators"]},{"metadata":{"id":"E2xmJAtxrDj4","colab_type":"code","colab":{}},"cell_type":"code","source":["from xgboost import XGBRegressor as XGBR\n","from sklearn.ensemble import RandomForestRegressor as RFR\n","from sklearn.linear_model import LinearRegression as LinearR\n","from sklearn.datasets import load_boston\n","from sklearn.model_selection import KFold, cross_val_score as CVS, train_test_split as TTS\n","from sklearn.metrics import mean_squared_error as MSE\n","import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","from time import time\n","import datetime"],"execution_count":0,"outputs":[]},{"metadata":{"id":"xR9yJko1rDj9","colab_type":"code","colab":{}},"cell_type":"code","source":["data = load_boston()\n","#波士顿数据集非常简单，但它所涉及到的问题却很多"],"execution_count":0,"outputs":[]},{"metadata":{"id":"jv0B5g2QrDkA","colab_type":"code","outputId":"3f202e1e-1616-4362-8ba8-6cbb749cc16a","executionInfo":{"status":"ok","timestamp":1550300075036,"user_tz":-480,"elapsed":850,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":1171}},"cell_type":"code","source":["data"],"execution_count":3,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'DESCR': \".. _boston_dataset:\\n\\nBoston house prices dataset\\n---------------------------\\n\\n**Data Set Characteristics:**  \\n\\n    :Number of Instances: 506 \\n\\n    :Number of Attributes: 13 numeric/categorical predictive. Median Value (attribute 14) is usually the target.\\n\\n    :Attribute Information (in order):\\n        - CRIM     per capita crime rate by town\\n        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.\\n        - INDUS    proportion of non-retail business acres per town\\n        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\\n        - NOX      nitric oxides concentration (parts per 10 million)\\n        - RM       average number of rooms per dwelling\\n        - AGE      proportion of owner-occupied units built prior to 1940\\n        - DIS      weighted distances to five Boston employment centres\\n        - RAD      index of accessibility to radial highways\\n        - TAX      full-value property-tax rate per $10,000\\n        - PTRATIO  pupil-teacher ratio by town\\n        - B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\\n        - LSTAT    % lower status of the population\\n        - MEDV     Median value of owner-occupied homes in $1000's\\n\\n    :Missing Attribute Values: None\\n\\n    :Creator: Harrison, D. and Rubinfeld, D.L.\\n\\nThis is a copy of UCI ML housing dataset.\\nhttps://archive.ics.uci.edu/ml/machine-learning-databases/housing/\\n\\n\\nThis dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\\n\\nThe Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\\nprices and the demand for clean air', J. Environ. Economics & Management,\\nvol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\\n...', Wiley, 1980.   N.B. Various transformations are used in the table on\\npages 244-261 of the latter.\\n\\nThe Boston house-price data has been used in many machine learning papers that address regression\\nproblems.   \\n     \\n.. topic:: References\\n\\n   - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\\n   - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\\n\",\n"," 'data': array([[6.3200e-03, 1.8000e+01, 2.3100e+00, ..., 1.5300e+01, 3.9690e+02,\n","         4.9800e+00],\n","        [2.7310e-02, 0.0000e+00, 7.0700e+00, ..., 1.7800e+01, 3.9690e+02,\n","         9.1400e+00],\n","        [2.7290e-02, 0.0000e+00, 7.0700e+00, ..., 1.7800e+01, 3.9283e+02,\n","         4.0300e+00],\n","        ...,\n","        [6.0760e-02, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9690e+02,\n","         5.6400e+00],\n","        [1.0959e-01, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9345e+02,\n","         6.4800e+00],\n","        [4.7410e-02, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9690e+02,\n","         7.8800e+00]]),\n"," 'feature_names': array(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD',\n","        'TAX', 'PTRATIO', 'B', 'LSTAT'], dtype='<U7'),\n"," 'filename': '/usr/local/lib/python3.6/dist-packages/sklearn/datasets/data/boston_house_prices.csv',\n"," 'target': array([24. , 21.6, 34.7, 33.4, 36.2, 28.7, 22.9, 27.1, 16.5, 18.9, 15. ,\n","        18.9, 21.7, 20.4, 18.2, 19.9, 23.1, 17.5, 20.2, 18.2, 13.6, 19.6,\n","        15.2, 14.5, 15.6, 13.9, 16.6, 14.8, 18.4, 21. , 12.7, 14.5, 13.2,\n","        13.1, 13.5, 18.9, 20. , 21. , 24.7, 30.8, 34.9, 26.6, 25.3, 24.7,\n","        21.2, 19.3, 20. , 16.6, 14.4, 19.4, 19.7, 20.5, 25. , 23.4, 18.9,\n","        35.4, 24.7, 31.6, 23.3, 19.6, 18.7, 16. , 22.2, 25. , 33. , 23.5,\n","        19.4, 22. , 17.4, 20.9, 24.2, 21.7, 22.8, 23.4, 24.1, 21.4, 20. ,\n","        20.8, 21.2, 20.3, 28. , 23.9, 24.8, 22.9, 23.9, 26.6, 22.5, 22.2,\n","        23.6, 28.7, 22.6, 22. , 22.9, 25. , 20.6, 28.4, 21.4, 38.7, 43.8,\n","        33.2, 27.5, 26.5, 18.6, 19.3, 20.1, 19.5, 19.5, 20.4, 19.8, 19.4,\n","        21.7, 22.8, 18.8, 18.7, 18.5, 18.3, 21.2, 19.2, 20.4, 19.3, 22. ,\n","        20.3, 20.5, 17.3, 18.8, 21.4, 15.7, 16.2, 18. , 14.3, 19.2, 19.6,\n","        23. , 18.4, 15.6, 18.1, 17.4, 17.1, 13.3, 17.8, 14. , 14.4, 13.4,\n","        15.6, 11.8, 13.8, 15.6, 14.6, 17.8, 15.4, 21.5, 19.6, 15.3, 19.4,\n","        17. , 15.6, 13.1, 41.3, 24.3, 23.3, 27. , 50. , 50. , 50. , 22.7,\n","        25. , 50. , 23.8, 23.8, 22.3, 17.4, 19.1, 23.1, 23.6, 22.6, 29.4,\n","        23.2, 24.6, 29.9, 37.2, 39.8, 36.2, 37.9, 32.5, 26.4, 29.6, 50. ,\n","        32. , 29.8, 34.9, 37. , 30.5, 36.4, 31.1, 29.1, 50. , 33.3, 30.3,\n","        34.6, 34.9, 32.9, 24.1, 42.3, 48.5, 50. , 22.6, 24.4, 22.5, 24.4,\n","        20. , 21.7, 19.3, 22.4, 28.1, 23.7, 25. , 23.3, 28.7, 21.5, 23. ,\n","        26.7, 21.7, 27.5, 30.1, 44.8, 50. , 37.6, 31.6, 46.7, 31.5, 24.3,\n","        31.7, 41.7, 48.3, 29. , 24. , 25.1, 31.5, 23.7, 23.3, 22. , 20.1,\n","        22.2, 23.7, 17.6, 18.5, 24.3, 20.5, 24.5, 26.2, 24.4, 24.8, 29.6,\n","        42.8, 21.9, 20.9, 44. , 50. , 36. , 30.1, 33.8, 43.1, 48.8, 31. ,\n","        36.5, 22.8, 30.7, 50. , 43.5, 20.7, 21.1, 25.2, 24.4, 35.2, 32.4,\n","        32. , 33.2, 33.1, 29.1, 35.1, 45.4, 35.4, 46. , 50. , 32.2, 22. ,\n","        20.1, 23.2, 22.3, 24.8, 28.5, 37.3, 27.9, 23.9, 21.7, 28.6, 27.1,\n","        20.3, 22.5, 29. , 24.8, 22. , 26.4, 33.1, 36.1, 28.4, 33.4, 28.2,\n","        22.8, 20.3, 16.1, 22.1, 19.4, 21.6, 23.8, 16.2, 17.8, 19.8, 23.1,\n","        21. , 23.8, 23.1, 20.4, 18.5, 25. , 24.6, 23. , 22.2, 19.3, 22.6,\n","        19.8, 17.1, 19.4, 22.2, 20.7, 21.1, 19.5, 18.5, 20.6, 19. , 18.7,\n","        32.7, 16.5, 23.9, 31.2, 17.5, 17.2, 23.1, 24.5, 26.6, 22.9, 24.1,\n","        18.6, 30.1, 18.2, 20.6, 17.8, 21.7, 22.7, 22.6, 25. , 19.9, 20.8,\n","        16.8, 21.9, 27.5, 21.9, 23.1, 50. , 50. , 50. , 50. , 50. , 13.8,\n","        13.8, 15. , 13.9, 13.3, 13.1, 10.2, 10.4, 10.9, 11.3, 12.3,  8.8,\n","         7.2, 10.5,  7.4, 10.2, 11.5, 15.1, 23.2,  9.7, 13.8, 12.7, 13.1,\n","        12.5,  8.5,  5. ,  6.3,  5.6,  7.2, 12.1,  8.3,  8.5,  5. , 11.9,\n","        27.9, 17.2, 27.5, 15. , 17.2, 17.9, 16.3,  7. ,  7.2,  7.5, 10.4,\n","         8.8,  8.4, 16.7, 14.2, 20.8, 13.4, 11.7,  8.3, 10.2, 10.9, 11. ,\n","         9.5, 14.5, 14.1, 16.1, 14.3, 11.7, 13.4,  9.6,  8.7,  8.4, 12.8,\n","        10.5, 17.1, 18.4, 15.4, 10.8, 11.8, 14.9, 12.6, 14.1, 13. , 13.4,\n","        15.2, 16.1, 17.8, 14.9, 14.1, 12.7, 13.5, 14.9, 20. , 16.4, 17.7,\n","        19.5, 20.2, 21.4, 19.9, 19. , 19.1, 19.1, 20.1, 19.9, 19.6, 23.2,\n","        29.8, 13.8, 13.3, 16.7, 12. , 14.6, 21.4, 23. , 23.7, 25. , 21.8,\n","        20.6, 21.2, 19.1, 20.6, 15.2,  7. ,  8.1, 13.6, 20.1, 21.8, 24.5,\n","        23.1, 19.7, 18.3, 21.2, 17.5, 16.8, 22.4, 20.6, 23.9, 22. , 11.9])}"]},"metadata":{"tags":[]},"execution_count":3}]},{"metadata":{"id":"6D6LXoFVrDkG","colab_type":"code","colab":{}},"cell_type":"code","source":["X = data.data\n","y = data.target"],"execution_count":0,"outputs":[]},{"metadata":{"id":"PR7OAjqerDkK","colab_type":"code","outputId":"7780727d-ab24-4705-f23b-182c38894c8e","executionInfo":{"status":"ok","timestamp":1550300081218,"user_tz":-480,"elapsed":598,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"cell_type":"code","source":["X.shape"],"execution_count":5,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(506, 13)"]},"metadata":{"tags":[]},"execution_count":5}]},{"metadata":{"id":"V4kUBguxrDkO","colab_type":"code","outputId":"189e3324-99ec-4413-df47-fab5ee9110bf","executionInfo":{"status":"ok","timestamp":1550300082639,"user_tz":-480,"elapsed":527,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"cell_type":"code","source":["y.shape"],"execution_count":6,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(506,)"]},"metadata":{"tags":[]},"execution_count":6}]},{"metadata":{"id":"_1_3SVtirDkS","colab_type":"code","outputId":"1506b7eb-a8b4-46c4-aba0-9c9ccc137b81","executionInfo":{"status":"ok","timestamp":1550300084106,"user_tz":-480,"elapsed":555,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":845}},"cell_type":"code","source":["y"],"execution_count":7,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([24. , 21.6, 34.7, 33.4, 36.2, 28.7, 22.9, 27.1, 16.5, 18.9, 15. ,\n","       18.9, 21.7, 20.4, 18.2, 19.9, 23.1, 17.5, 20.2, 18.2, 13.6, 19.6,\n","       15.2, 14.5, 15.6, 13.9, 16.6, 14.8, 18.4, 21. , 12.7, 14.5, 13.2,\n","       13.1, 13.5, 18.9, 20. , 21. , 24.7, 30.8, 34.9, 26.6, 25.3, 24.7,\n","       21.2, 19.3, 20. , 16.6, 14.4, 19.4, 19.7, 20.5, 25. , 23.4, 18.9,\n","       35.4, 24.7, 31.6, 23.3, 19.6, 18.7, 16. , 22.2, 25. , 33. , 23.5,\n","       19.4, 22. , 17.4, 20.9, 24.2, 21.7, 22.8, 23.4, 24.1, 21.4, 20. ,\n","       20.8, 21.2, 20.3, 28. , 23.9, 24.8, 22.9, 23.9, 26.6, 22.5, 22.2,\n","       23.6, 28.7, 22.6, 22. , 22.9, 25. , 20.6, 28.4, 21.4, 38.7, 43.8,\n","       33.2, 27.5, 26.5, 18.6, 19.3, 20.1, 19.5, 19.5, 20.4, 19.8, 19.4,\n","       21.7, 22.8, 18.8, 18.7, 18.5, 18.3, 21.2, 19.2, 20.4, 19.3, 22. ,\n","       20.3, 20.5, 17.3, 18.8, 21.4, 15.7, 16.2, 18. , 14.3, 19.2, 19.6,\n","       23. , 18.4, 15.6, 18.1, 17.4, 17.1, 13.3, 17.8, 14. , 14.4, 13.4,\n","       15.6, 11.8, 13.8, 15.6, 14.6, 17.8, 15.4, 21.5, 19.6, 15.3, 19.4,\n","       17. , 15.6, 13.1, 41.3, 24.3, 23.3, 27. , 50. , 50. , 50. , 22.7,\n","       25. , 50. , 23.8, 23.8, 22.3, 17.4, 19.1, 23.1, 23.6, 22.6, 29.4,\n","       23.2, 24.6, 29.9, 37.2, 39.8, 36.2, 37.9, 32.5, 26.4, 29.6, 50. ,\n","       32. , 29.8, 34.9, 37. , 30.5, 36.4, 31.1, 29.1, 50. , 33.3, 30.3,\n","       34.6, 34.9, 32.9, 24.1, 42.3, 48.5, 50. , 22.6, 24.4, 22.5, 24.4,\n","       20. , 21.7, 19.3, 22.4, 28.1, 23.7, 25. , 23.3, 28.7, 21.5, 23. ,\n","       26.7, 21.7, 27.5, 30.1, 44.8, 50. , 37.6, 31.6, 46.7, 31.5, 24.3,\n","       31.7, 41.7, 48.3, 29. , 24. , 25.1, 31.5, 23.7, 23.3, 22. , 20.1,\n","       22.2, 23.7, 17.6, 18.5, 24.3, 20.5, 24.5, 26.2, 24.4, 24.8, 29.6,\n","       42.8, 21.9, 20.9, 44. , 50. , 36. , 30.1, 33.8, 43.1, 48.8, 31. ,\n","       36.5, 22.8, 30.7, 50. , 43.5, 20.7, 21.1, 25.2, 24.4, 35.2, 32.4,\n","       32. , 33.2, 33.1, 29.1, 35.1, 45.4, 35.4, 46. , 50. , 32.2, 22. ,\n","       20.1, 23.2, 22.3, 24.8, 28.5, 37.3, 27.9, 23.9, 21.7, 28.6, 27.1,\n","       20.3, 22.5, 29. , 24.8, 22. , 26.4, 33.1, 36.1, 28.4, 33.4, 28.2,\n","       22.8, 20.3, 16.1, 22.1, 19.4, 21.6, 23.8, 16.2, 17.8, 19.8, 23.1,\n","       21. , 23.8, 23.1, 20.4, 18.5, 25. , 24.6, 23. , 22.2, 19.3, 22.6,\n","       19.8, 17.1, 19.4, 22.2, 20.7, 21.1, 19.5, 18.5, 20.6, 19. , 18.7,\n","       32.7, 16.5, 23.9, 31.2, 17.5, 17.2, 23.1, 24.5, 26.6, 22.9, 24.1,\n","       18.6, 30.1, 18.2, 20.6, 17.8, 21.7, 22.7, 22.6, 25. , 19.9, 20.8,\n","       16.8, 21.9, 27.5, 21.9, 23.1, 50. , 50. , 50. , 50. , 50. , 13.8,\n","       13.8, 15. , 13.9, 13.3, 13.1, 10.2, 10.4, 10.9, 11.3, 12.3,  8.8,\n","        7.2, 10.5,  7.4, 10.2, 11.5, 15.1, 23.2,  9.7, 13.8, 12.7, 13.1,\n","       12.5,  8.5,  5. ,  6.3,  5.6,  7.2, 12.1,  8.3,  8.5,  5. , 11.9,\n","       27.9, 17.2, 27.5, 15. , 17.2, 17.9, 16.3,  7. ,  7.2,  7.5, 10.4,\n","        8.8,  8.4, 16.7, 14.2, 20.8, 13.4, 11.7,  8.3, 10.2, 10.9, 11. ,\n","        9.5, 14.5, 14.1, 16.1, 14.3, 11.7, 13.4,  9.6,  8.7,  8.4, 12.8,\n","       10.5, 17.1, 18.4, 15.4, 10.8, 11.8, 14.9, 12.6, 14.1, 13. , 13.4,\n","       15.2, 16.1, 17.8, 14.9, 14.1, 12.7, 13.5, 14.9, 20. , 16.4, 17.7,\n","       19.5, 20.2, 21.4, 19.9, 19. , 19.1, 19.1, 20.1, 19.9, 19.6, 23.2,\n","       29.8, 13.8, 13.3, 16.7, 12. , 14.6, 21.4, 23. , 23.7, 25. , 21.8,\n","       20.6, 21.2, 19.1, 20.6, 15.2,  7. ,  8.1, 13.6, 20.1, 21.8, 24.5,\n","       23.1, 19.7, 18.3, 21.2, 17.5, 16.8, 22.4, 20.6, 23.9, 22. , 11.9])"]},"metadata":{"tags":[]},"execution_count":7}]},{"metadata":{"id":"Su8PT-1krDkW","colab_type":"code","colab":{}},"cell_type":"code","source":["Xtrain,Xtest,Ytrain,Ytest = TTS(X,y,test_size=0.3,random_state=420)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"JPSXYe8XrDkd","colab_type":"code","colab":{}},"cell_type":"code","source":["reg = XGBR(n_estimators=100).fit(Xtrain,Ytrain) #训练"],"execution_count":0,"outputs":[]},{"metadata":{"id":"XQbGiNazrDkk","colab_type":"code","outputId":"d2639f59-634a-4ff7-87ce-4e1bdf2737a8","executionInfo":{"status":"ok","timestamp":1550300092417,"user_tz":-480,"elapsed":526,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":575}},"cell_type":"code","source":["reg.predict(Xtest) #传统接口predict"],"execution_count":10,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([ 8.165384 , 21.919355 , 29.843645 , 11.874415 ,  8.833874 ,\n","       20.698246 , 15.456877 , 15.544203 , 15.273806 , 13.444421 ,\n","       22.130966 , 35.072395 , 21.383947 , 27.477697 , 20.449163 ,\n","       10.434615 , 19.13851  , 24.973454 , 23.284975 , 23.22411  ,\n","       17.86431  , 17.218367 , 25.284815 , 20.962675 , 20.506361 ,\n","       16.18782  , 21.71437  , 31.687273 , 22.739857 , 15.976351 ,\n","       37.61867  , 20.701538 , 21.191647 , 23.53703  , 23.374733 ,\n","       24.682228 , 16.27922  , 24.404453 , 16.918646 , 34.06889  ,\n","       18.060398 , 21.352114 , 37.74781  , 17.90909  , 14.035863 ,\n","       28.243176 , 46.44803  , 14.748789 , 10.719417 , 35.26486  ,\n","       25.46181  , 21.976503 , 20.583235 , 49.3701   , 26.799538 ,\n","       26.286161 , 17.937538 , 20.566235 , 16.813719 , 18.816374 ,\n","       14.940857 , 22.213655 , 19.239632 , 30.246548 , 27.522081 ,\n","       18.951006 , 19.352182 , 15.716684 , 22.732222 , 19.14629  ,\n","       29.943521 , 43.593327 , 29.910528 , 22.987352 , 20.6698   ,\n","       23.078789 , 42.398773 , 26.09842  , 21.91394  , 20.648191 ,\n","       19.003222 , 46.083096 , 22.536583 ,  8.913077 , 26.097902 ,\n","       23.269075 , 17.367735 , 21.026497 , 19.989    , 11.0460415,\n","       20.702105 , 15.85642  , 41.646564 , 33.36533  , 22.891464 ,\n","       10.986401 , 15.041652 , 20.024511 ,  8.583407 , 10.585413 ,\n","       31.516539 , 17.450106 , 24.464012 , 23.16676  , 31.216547 ,\n","       41.60174  , 21.293688 ,  8.508124 , 22.987352 , 14.409528 ,\n","       45.271053 , 21.575006 , 22.017088 , 22.765581 , 18.600304 ,\n","       27.935238 , 24.568512 , 17.984907 , 44.804962 , 17.453247 ,\n","       24.616978 , 22.51571  , 16.867426 , 16.851673 , 15.302382 ,\n","       22.844278 , 32.24536  ,  9.803618 , 21.071335 , 19.696089 ,\n","       16.056892 , 19.578579 ,  9.987525 , 28.184805 , 29.464226 ,\n","       20.240805 , 19.482244 , 15.280221 ,  9.401806 , 17.187815 ,\n","       42.554493 , 16.713844 , 23.872856 , 19.95768  , 30.148867 ,\n","       20.396807 , 13.163115 , 40.93572  , 25.202625 , 21.823097 ,\n","       14.690604 , 26.191984 ], dtype=float32)"]},"metadata":{"tags":[]},"execution_count":10}]},{"metadata":{"id":"Jzcy8gtOrDko","colab_type":"code","outputId":"5aa67bdc-926b-4d8c-8a8a-5b1e27a0b267","executionInfo":{"status":"ok","timestamp":1550300111114,"user_tz":-480,"elapsed":820,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"cell_type":"code","source":["reg.score(Xtest,Ytest) #你能想出这里应该返回什么模型评估指标么？"],"execution_count":11,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.9197580267581366"]},"metadata":{"tags":[]},"execution_count":11}]},{"metadata":{"id":"wTY8sCO9rDkt","colab_type":"code","outputId":"613fc4f7-4d48-4391-a918-79420f72d8a1","executionInfo":{"status":"ok","timestamp":1550300112491,"user_tz":-480,"elapsed":614,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"cell_type":"code","source":["y.mean()"],"execution_count":12,"outputs":[{"output_type":"execute_result","data":{"text/plain":["22.532806324110677"]},"metadata":{"tags":[]},"execution_count":12}]},{"metadata":{"id":"63msdZE8rDkw","colab_type":"code","outputId":"de87f69d-aa20-4821-fe63-ec4b1edb4823","executionInfo":{"status":"ok","timestamp":1550300114404,"user_tz":-480,"elapsed":780,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"cell_type":"code","source":["MSE(Ytest,reg.predict(Xtest))"],"execution_count":13,"outputs":[{"output_type":"execute_result","data":{"text/plain":["7.466827353555599"]},"metadata":{"tags":[]},"execution_count":13}]},{"metadata":{"id":"_uX4dCKorDk1","colab_type":"code","outputId":"edcc4d51-6924-41db-d52a-16bc0e6b3aaf","executionInfo":{"status":"ok","timestamp":1550209382288,"user_tz":-480,"elapsed":612,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":71}},"cell_type":"code","source":["reg.feature_importances_ #树模型的优势之一：能够查看模型的重要性分数，可以使用嵌入法(SelectFromModel)进行特征选择"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0.13862929, 0.0046729 , 0.02803738, 0.00623053, 0.08411215,\n","       0.16510904, 0.08566978, 0.15109034, 0.03271028, 0.0623053 ,\n","       0.06074766, 0.07632399, 0.10436137], dtype=float32)"]},"metadata":{"tags":[]},"execution_count":14}]},{"metadata":{"id":"jK2kL4iGrDlN","colab_type":"code","colab":{}},"cell_type":"code","source":["reg = XGBR(n_estimators=100) #交叉验证中导入的没有经过训练的模型"],"execution_count":0,"outputs":[]},{"metadata":{"id":"aR-t3nnYrDlR","colab_type":"code","outputId":"3cecc5d0-a6b4-42a3-b9bf-704a9db3ffa0","executionInfo":{"status":"ok","timestamp":1550209386583,"user_tz":-480,"elapsed":614,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"cell_type":"code","source":["CVS(reg,Xtrain,Ytrain,cv=5).mean()\n","#这里应该返回什么模型评估指标，还记得么？"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.8017863029875325"]},"metadata":{"tags":[]},"execution_count":16}]},{"metadata":{"id":"h5eTWaeirDlV","colab_type":"code","outputId":"c027f408-4b18-4b92-e515-d33188a35333","executionInfo":{"status":"ok","timestamp":1550209405924,"user_tz":-480,"elapsed":641,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"cell_type":"code","source":["#严谨的交叉验证与不严谨的交叉验证之间的讨论：训练集 or 全数据？\n","CVS(reg,Xtrain,Ytrain,cv=5)"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0.83340801, 0.77096033, 0.83473392, 0.80424149, 0.76558778])"]},"metadata":{"tags":[]},"execution_count":17}]},{"metadata":{"id":"5WKfI4c2rDlc","colab_type":"code","colab":{}},"cell_type":"code","source":["#严谨 vs 不严谨"],"execution_count":0,"outputs":[]},{"metadata":{"id":"kT05lT27rDlf","colab_type":"code","outputId":"93c47be6-092d-4760-81aa-71045dd99536","executionInfo":{"status":"ok","timestamp":1550209409953,"user_tz":-480,"elapsed":622,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"cell_type":"code","source":["CVS(reg,Xtrain,Ytrain,cv=5,scoring='neg_mean_squared_error').mean()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["-16.041115480238048"]},"metadata":{"tags":[]},"execution_count":19}]},{"metadata":{"id":"FlXlOe0JrDlk","colab_type":"code","outputId":"22b8bb1a-1899-48d5-b581-729e2f7f5cc9","executionInfo":{"status":"ok","timestamp":1550209421163,"user_tz":-480,"elapsed":643,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":647}},"cell_type":"code","source":["#来查看一下sklearn中所有的模型评估指标\n","import sklearn\n","sorted(sklearn.metrics.SCORERS.keys())"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['accuracy',\n"," 'adjusted_mutual_info_score',\n"," 'adjusted_rand_score',\n"," 'average_precision',\n"," 'balanced_accuracy',\n"," 'brier_score_loss',\n"," 'completeness_score',\n"," 'explained_variance',\n"," 'f1',\n"," 'f1_macro',\n"," 'f1_micro',\n"," 'f1_samples',\n"," 'f1_weighted',\n"," 'fowlkes_mallows_score',\n"," 'homogeneity_score',\n"," 'mutual_info_score',\n"," 'neg_log_loss',\n"," 'neg_mean_absolute_error',\n"," 'neg_mean_squared_error',\n"," 'neg_mean_squared_log_error',\n"," 'neg_median_absolute_error',\n"," 'normalized_mutual_info_score',\n"," 'precision',\n"," 'precision_macro',\n"," 'precision_micro',\n"," 'precision_samples',\n"," 'precision_weighted',\n"," 'r2',\n"," 'recall',\n"," 'recall_macro',\n"," 'recall_micro',\n"," 'recall_samples',\n"," 'recall_weighted',\n"," 'roc_auc',\n"," 'v_measure_score']"]},"metadata":{"tags":[]},"execution_count":20}]},{"metadata":{"id":"XYKwE3b5rDls","colab_type":"code","outputId":"ded791ad-24a7-47ba-8947-cb8929644e45","executionInfo":{"status":"ok","timestamp":1550209425752,"user_tz":-480,"elapsed":2063,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"cell_type":"code","source":["#使用随机森林和线性回归进行一个对比\n","rfr = RFR(n_estimators=100)\n","CVS(rfr,Xtrain,Ytrain,cv=5).mean()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.8040718641989123"]},"metadata":{"tags":[]},"execution_count":21}]},{"metadata":{"id":"sf1VaZilrDlw","colab_type":"code","outputId":"66c3d172-b213-4293-dc78-2878e2818924","executionInfo":{"status":"ok","timestamp":1550209428552,"user_tz":-480,"elapsed":1619,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"cell_type":"code","source":["CVS(rfr,Xtrain,Ytrain,cv=5,scoring='neg_mean_squared_error').mean()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["-16.704096825231385"]},"metadata":{"tags":[]},"execution_count":22}]},{"metadata":{"id":"-KpaCxp-rDl1","colab_type":"code","outputId":"79c20f27-9637-4d85-ed5e-6fc938b0ca62","executionInfo":{"status":"ok","timestamp":1550209432432,"user_tz":-480,"elapsed":837,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"cell_type":"code","source":["lr = LinearR()\n","CVS(lr,Xtrain,Ytrain,cv=5).mean()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.6835070597278082"]},"metadata":{"tags":[]},"execution_count":23}]},{"metadata":{"id":"CC8WfbofrDl6","colab_type":"code","outputId":"3f77c485-58ef-4c0b-c0cc-42345f82c3b4","executionInfo":{"status":"ok","timestamp":1550209434337,"user_tz":-480,"elapsed":636,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"cell_type":"code","source":["CVS(lr,Xtrain,Ytrain,cv=5,scoring='neg_mean_squared_error').mean()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["-25.349507493648453"]},"metadata":{"tags":[]},"execution_count":24}]},{"metadata":{"id":"UaFwb5bMrDl-","colab_type":"code","colab":{}},"cell_type":"code","source":["#如果开启参数slient：在数据巨大，预料到算法运行会非常缓慢的时候可以使用这个参数来监控模型的训练进度\n","reg = XGBR(n_estimators=10,silent=False)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"oV9BlWmkFxid","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":35},"outputId":"c9785d82-4d8c-45af-f238-7663ddd825e4","executionInfo":{"status":"ok","timestamp":1550300166021,"user_tz":-480,"elapsed":844,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["CVS(reg,Xtrain,Ytrain,cv=5,scoring='neg_mean_squared_error').mean()"],"execution_count":17,"outputs":[{"output_type":"execute_result","data":{"text/plain":["-92.67865836936579"]},"metadata":{"tags":[]},"execution_count":17}]},{"metadata":{"id":"6IZ86MPTrDmC","colab_type":"code","colab":{}},"cell_type":"code","source":["def plot_learning_curve(estimator,title, X, y, \n","                        ax=None, #选择子图\n","                        ylim=None, #设置纵坐标的取值范围\n","                        cv=None, #交叉验证\n","                        n_jobs=None #设定索要使用的线程\n","                       ):\n","    \n","    from sklearn.model_selection import learning_curve\n","    import matplotlib.pyplot as plt\n","    import numpy as np\n","    \n","    train_sizes, train_scores, test_scores = learning_curve(estimator, X, y\n","                                                            ,shuffle=True\n","                                                            ,cv=cv\n","                                                            ,random_state=420\n","                                                            ,n_jobs=n_jobs) \n","    print(train_sizes)\n","    if ax == None:\n","        ax = plt.gca()\n","    else:\n","        ax = plt.figure()\n","    ax.set_title(title)\n","    if ylim is not None:\n","        ax.set_ylim(*ylim)\n","    ax.set_xlabel(\"Training examples\")\n","    ax.set_ylabel(\"Score\")\n","    ax.grid() #绘制网格，不是必须\n","    ax.plot(train_sizes, np.mean(train_scores, axis=1), 'o-'\n","            , color=\"r\",label=\"Training score\")\n","    ax.plot(train_sizes, np.mean(test_scores, axis=1), 'o-'\n","            , color=\"g\",label=\"Test score\")\n","    ax.legend(loc=\"best\")\n","    return ax"],"execution_count":0,"outputs":[]},{"metadata":{"id":"UjoW8fkrrDmG","colab_type":"code","colab":{}},"cell_type":"code","source":["cv = KFold(n_splits=5, shuffle = True, random_state=42) #交叉验证模式"],"execution_count":0,"outputs":[]},{"metadata":{"id":"l_AzzivO7uxv","colab_type":"code","outputId":"6b371696-4ec5-42e4-fbb2-01487cc15f8a","executionInfo":{"status":"ok","timestamp":1550300173750,"user_tz":-480,"elapsed":523,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"cell_type":"code","source":["Xtrain.shape"],"execution_count":19,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(354, 13)"]},"metadata":{"tags":[]},"execution_count":19}]},{"metadata":{"id":"ZrYVe82SrDmK","colab_type":"code","outputId":"1a961f35-ac98-4a16-987b-32d37b715510","executionInfo":{"status":"ok","timestamp":1550300176268,"user_tz":-480,"elapsed":1657,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":394}},"cell_type":"code","source":["plot_learning_curve(XGBR(n_estimators=100,random_state=420)\n","                    ,\"XGB\",Xtrain,Ytrain,ax=None,cv=cv)\n","plt.show()"],"execution_count":20,"outputs":[{"output_type":"stream","text":["[ 28  91 155 219 283]\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAe8AAAFnCAYAAACPasF4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDG8iIiKRY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mZmJgoKCnDx4kXtMHlBQQGKiorqpUCixkQQBJxLOYuwuFAcuBGObGU2AMCl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\nw/AmIiISGYY3ERGRyDC8iYiIROb/AeQ5cILHA+IPAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 576x396 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"metadata":{"id":"E3MhJvZMrDmO","colab_type":"code","outputId":"09281513-0af6-41f5-ead0-4fe2b8ddb93a","executionInfo":{"status":"ok","timestamp":1550300193544,"user_tz":-480,"elapsed":12548,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":358}},"cell_type":"code","source":["#=====【TIME WARNING：25 seconds】=====#\n","\n","axisx = range(10,1010,50)\n","rs = []\n","for i in axisx:\n","    reg = XGBR(n_estimators=i,random_state=420)\n","    rs.append(CVS(reg,Xtrain,Ytrain,cv=cv).mean())\n","print(axisx[rs.index(max(rs))],max(rs))\n","plt.figure(figsize=(20,5))\n","plt.plot(axisx,rs,c=\"red\",label=\"XGB\")\n","plt.legend()\n","plt.show()"],"execution_count":21,"outputs":[{"output_type":"stream","text":["660 0.8046775284172915\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABIgAAAEvCAYAAAAuDfoyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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w4cKUSqUsXrw4K1asSFtbWy666KJcd911+eAHP5gkecMb3pBTTz21pkWPFeXuzlRLpVTmzC26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\nY+sTKWAAAAAASUVORK5CYII=\n","text/plain":["<Figure size 1440x360 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"metadata":{"id":"Zhy3gG9trDmT","colab_type":"code","colab":{}},"cell_type":"code","source":["#选出来的n_estimators非常不寻常，我们是否要选择准确率最高的n_estimators值呢？"],"execution_count":0,"outputs":[]},{"metadata":{"id":"b2hNKazZt5Wr","colab_type":"text"},"cell_type":"markdown","source":["### 进化的学习曲线：方差与泛化误差"]},{"metadata":{"id":"1YTz6lk5rDmW","colab_type":"code","outputId":"a1fbdf3e-a278-47d6-f83d-b99c025c2106","executionInfo":{"status":"ok","timestamp":1550210791521,"user_tz":-480,"elapsed":13042,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":394}},"cell_type":"code","source":["#======【TIME WARNING: 20s】=======#\n","axisx = range(50,1050,50)\n","rs = []\n","var = []\n","ge = []\n","for i in axisx:\n","    reg = XGBR(n_estimators=i,random_state=420)\n","    cvresult = CVS(reg,Xtrain,Ytrain,cv=cv)\n","    #记录1-偏差\n","    rs.append(cvresult.mean())\n","    #记录方差\n","    var.append(cvresult.var())\n","    #计算泛化误差的可控部分\n","    ge.append((1 - cvresult.mean())**2+cvresult.var())\n","#打印R2最高所对应的参数取值，并打印这个参数下的方差\n","print(axisx[rs.index(max(rs))],max(rs),var[rs.index(max(rs))])\n","#打印方差最低时对应的参数取值，并打印这个参数下的R2\n","print(axisx[var.index(min(var))],rs[var.index(min(var))],min(var))\n","#打印泛化误差可控部分的参数取值，并打印这个参数下的R2，方差以及泛化误差的可控部分\n","print(axisx[ge.index(min(ge))],rs[ge.index(min(ge))],var[ge.index(min(ge))],min(ge))\n","plt.figure(figsize=(20,5))\n","plt.plot(axisx,rs,c=\"red\",label=\"XGB\")\n","plt.legend()\n","plt.show()"],"execution_count":0,"outputs":[{"output_type":"stream","text":["650 0.80476050359201 0.01053673846018678\n","50 0.7857724708830981 0.009072727885598212\n","150 0.8032842414878519 0.009747694343514357 0.04844478399052411\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABJAAAAEvCAYAAAAadDsuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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A4IkBCXDh27pDttE+B6TOsLgUAAAAAAMQZARLiwhWdvuYvnGVpHQAAAAAAIP4IkBAXzvIymYah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\nnwLij34FxB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size 1440x360 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"metadata":{"id":"c1HpTf1drDma","colab_type":"code","outputId":"76a0f08b-ed55-410b-8af1-91c3d7c582bf","executionInfo":{"status":"ok","timestamp":1550210847350,"user_tz":-480,"elapsed":5841,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":394}},"cell_type":"code","source":["axisx = range(100,300,10)\n","rs = []\n","var = []\n","ge = []\n","for i in axisx:\n","    reg = XGBR(n_estimators=i,random_state=420)\n","    cvresult = CVS(reg,Xtrain,Ytrain,cv=cv)\n","    rs.append(cvresult.mean())\n","    var.append(cvresult.var())\n","    ge.append((1 - cvresult.mean())**2+cvresult.var())\n","print(axisx[rs.index(max(rs))],max(rs),var[rs.index(max(rs))])\n","print(axisx[var.index(min(var))],rs[var.index(min(var))],min(var))\n","print(axisx[ge.index(min(ge))],rs[ge.index(min(ge))],var[ge.index(min(ge))],min(ge))\n","rs = np.array(rs)\n","var = np.array(var)*0.01\n","plt.figure(figsize=(20,5))\n","plt.plot(axisx,rs,c=\"black\",label=\"XGB\")\n","#添加方差线\n","plt.plot(axisx,rs+var,c=\"red\",linestyle='-.')\n","plt.plot(axisx,rs-var,c=\"red\",linestyle='-.')\n","plt.legend()\n","plt.show()"],"execution_count":0,"outputs":[{"output_type":"stream","text":["180 0.8038787848970184 0.00959321570484315\n","180 0.8038787848970184 0.00959321570484315\n","180 0.8038787848970184 0.00959321570484315 0.04805674671831314\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABJAAAAEvCAYAAAAadDsuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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QPHCqzYJ4zj17dMGzZRMQbb2CpUh2tVsusWfOpWrUCAwb0Zv/+oxgfLjm0WHCbNQ1VRDjR46ek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\napC6cRup/9lJZo9el1w8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FSBFRERERESkWKmA5C1mM5k9exMYGghaa+pdAQGcXf7anzbF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\n","text/plain":["<Figure size 1440x360 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"metadata":{"id":"DfCkgzhIrDmd","colab_type":"code","outputId":"148daa1c-1767-47f6-9702-27b323e69f73","executionInfo":{"status":"ok","timestamp":1550210918984,"user_tz":-480,"elapsed":1313,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":340}},"cell_type":"code","source":["#看看泛化误差的可控部分如何？\n","plt.figure(figsize=(20,5))\n","plt.plot(axisx,ge,c=\"gray\",linestyle='-.')\n","plt.show()"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABJUAAAEvCAYAAAD8XfBqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAHV1dXzve99j27Zt/Mu//At2ux2AnJwcHn30Udcfh8OBzWZj+/btPPLII2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wm5NZpXMi1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pYVGxzGcpp5eX1fXJbq6LpGUlOxWjQU1JmQiGhdyLSPGhNPpZHh4yFU0ev/9d7h0qZMHHngI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\nRU5ErkJggg==\n","text/plain":["<Figure size 1440x360 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"metadata":{"id":"Z5imTc7vrDmg","colab_type":"code","outputId":"5b34153a-6b55-488f-9796-dc147605761b","executionInfo":{"status":"ok","timestamp":1550210940914,"user_tz":-480,"elapsed":857,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":53}},"cell_type":"code","source":["#验证模型效果是否提高了？\n","time0 = time()\n","print(XGBR(n_estimators=100,random_state=420).fit(Xtrain,Ytrain).score(Xtest,Ytest))\n","print(time()-time0)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["0.9197580267581366\n","0.03966712951660156\n"],"name":"stdout"}]},{"metadata":{"id":"NxiZtn4mrDmj","colab_type":"code","outputId":"cac8fb06-6b3d-412d-b06a-722a435f82dd","executionInfo":{"status":"ok","timestamp":1550210944370,"user_tz":-480,"elapsed":628,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":53}},"cell_type":"code","source":["time0 = time()\n","print(XGBR(n_estimators=660,random_state=420).fit(Xtrain,Ytrain).score(Xtest,Ytest))\n","print(time()-time0)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["0.9208745746309475\n","0.19384145736694336\n"],"name":"stdout"}]},{"metadata":{"id":"jk0XeuhurDmn","colab_type":"code","outputId":"fd77119d-d4f5-47bb-9091-1923d0001af3","executionInfo":{"status":"ok","timestamp":1550211407585,"user_tz":-480,"elapsed":628,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":53}},"cell_type":"code","source":["time0 = time()\n","print(XGBR(n_estimators=180,random_state=420).fit(Xtrain,Ytrain).score(Xtest,Ytest))\n","print(time()-time0)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["0.9231068620728082\n","0.05786609649658203\n"],"name":"stdout"}]},{"metadata":{"id":"fPAUBD9dzPqP","colab_type":"text"},"cell_type":"markdown","source":["## 有放回随机抽样：重要参数subsample"]},{"metadata":{"id":"-JWGaHPhrDmr","colab_type":"code","outputId":"8621af73-cf1b-4da1-f916-def24dd29a6e","executionInfo":{"status":"ok","timestamp":1550214048667,"user_tz":-480,"elapsed":4950,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":358}},"cell_type":"code","source":["axisx = np.linspace(0,1,20)\n","rs = []\n","for i in axisx:\n","    reg = XGBR(n_estimators=180,subsample=i,random_state=420)\n","    rs.append(CVS(reg,Xtrain,Ytrain,cv=cv).mean())\n","print(axisx[rs.index(max(rs))],max(rs))\n","plt.figure(figsize=(20,5))\n","plt.plot(axisx,rs,c=\"green\",label=\"XGB\")\n","plt.legend()\n","plt.show()"],"execution_count":0,"outputs":[{"output_type":"stream","text":["0.5263157894736842 0.8356103920206508\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABHwAAAEvCAYAAAA3qtbRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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rEwVXZT0LfP369cOvP/WpT8Wf/dmfTRr2MN7Q+D0GbAYAAAAq7aie0kX5MtmOSDekY0XzyqRL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\nSUVORK5CYII=\n","text/plain":["<Figure size 1440x360 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"metadata":{"id":"vA_ncR4ArDm1","colab_type":"code","outputId":"0f8c83b0-052b-4532-9f37-64a71f5c621c","executionInfo":{"status":"ok","timestamp":1550214085223,"user_tz":-480,"elapsed":5316,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":394}},"cell_type":"code","source":["#继续细化学习曲线\n","axisx = np.linspace(0.05,1,20)\n","rs = []\n","var = []\n","ge = []\n","for i in axisx:\n","    reg = XGBR(n_estimators=180,subsample=i,random_state=420)\n","    cvresult = CVS(reg,Xtrain,Ytrain,cv=cv)\n","    rs.append(cvresult.mean())\n","    var.append(cvresult.var())\n","    ge.append((1 - cvresult.mean())**2+cvresult.var())\n","print(axisx[rs.index(max(rs))],max(rs),var[rs.index(max(rs))])\n","print(axisx[var.index(min(var))],rs[var.index(min(var))],min(var))\n","print(axisx[ge.index(min(ge))],rs[ge.index(min(ge))],var[ge.index(min(ge))],min(ge))\n","rs = np.array(rs)\n","var = np.array(var)\n","plt.figure(figsize=(20,5))\n","plt.plot(axisx,rs,c=\"black\",label=\"XGB\")\n","plt.plot(axisx,rs+var,c=\"red\",linestyle='-.')\n","plt.plot(axisx,rs-var,c=\"red\",linestyle='-.')\n","plt.legend()\n","plt.show()"],"execution_count":0,"outputs":[{"output_type":"stream","text":["0.5499999999999999 0.8373036973488803 0.004472634200958534\n","0.7999999999999999 0.8336086297533647 0.004388753745658516\n","0.5499999999999999 0.8373036973488803 0.004472634200958534 0.030942721097303266\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABIQAAAEvCAYAAAA0MRq8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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AQ1IkuK9SnSP3+6BpSEkBCZhD+duIRIDySm0oZy6xbG9WvQHzpA/Gefg6KgO3YU0+D+JHzYF8fL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size 1440x360 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"metadata":{"id":"onG9aGzarDm_","colab_type":"code","outputId":"e645dc47-c491-46ca-a2f6-8a5a2853a3a8","executionInfo":{"status":"ok","timestamp":1550214192859,"user_tz":-480,"elapsed":6772,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":394}},"cell_type":"code","source":["#细化学习曲线\n","axisx = np.linspace(0.75,1,25)\n","rs = []\n","var = []\n","ge = []\n","for i in axisx:\n","    reg = XGBR(n_estimators=180,subsample=i,random_state=420)\n","    cvresult = CVS(reg,Xtrain,Ytrain,cv=cv)\n","    rs.append(cvresult.mean())\n","    var.append(cvresult.var())\n","    ge.append((1 - cvresult.mean())**2+cvresult.var())\n","print(axisx[rs.index(max(rs))],max(rs),var[rs.index(max(rs))])\n","print(axisx[var.index(min(var))],rs[var.index(min(var))],min(var))\n","print(axisx[ge.index(min(ge))],rs[ge.index(min(ge))],var[ge.index(min(ge))],min(ge))\n","rs = np.array(rs)\n","var = np.array(var)\n","plt.figure(figsize=(20,5))\n","plt.plot(axisx,rs,c=\"black\",label=\"XGB\")\n","plt.plot(axisx,rs+var,c=\"red\",linestyle='-.')\n","plt.plot(axisx,rs-var,c=\"red\",linestyle='-.')\n","plt.legend()\n","plt.show()"],"execution_count":0,"outputs":[{"output_type":"stream","text":["0.8020833333333334 0.8352429067739564 0.003950440531134253\n","0.8020833333333334 0.8352429067739564 0.003950440531134253\n","0.8020833333333334 0.8352429067739564 0.003950440531134253 0.031095340299429473\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABIQAAAEvCAYAAAA0MRq8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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m7c6J5OYmIUpUoVRVm69NHHLVzouLaGD3/kYSkpKUq+fPkUf39/JSIiIu3xnD7tuH4L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fr8mSJSszZ84hLi6Orl07EB8fb6O0/03bpTvalm2gbl2xo1idtMuYhdl0JXK1GsHDE+fdO1Fv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\n","text/plain":["<Figure size 1440x360 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"metadata":{"id":"2jCFlmMbrDnE","colab_type":"code","outputId":"2666655a-b0cc-4272-af15-1297bfc8bf0a","executionInfo":{"status":"ok","timestamp":1550214258084,"user_tz":-480,"elapsed":613,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"cell_type":"code","source":["reg = XGBR(n_estimators=180\n","#           ,subsample=0.7708333333333334\n","           ,random_state=420).fit(Xtrain,Ytrain)\n","reg.score(Xtest,Ytest)"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.9231068620728082"]},"metadata":{"tags":[]},"execution_count":47}]},{"metadata":{"id":"teysiELkrDnM","colab_type":"code","outputId":"633aa541-104f-480b-e6ad-b8d01bce1d4a","executionInfo":{"status":"ok","timestamp":1550214274779,"user_tz":-480,"elapsed":618,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"cell_type":"code","source":["MSE(Ytest,reg.predict(Xtest))"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["7.155205217161047"]},"metadata":{"tags":[]},"execution_count":48}]},{"metadata":{"id":"qjb6SgUo-QKV","colab_type":"text"},"cell_type":"markdown","source":["## 迭代决策树：重要参数eta"]},{"metadata":{"id":"fh08RsXmrDnQ","colab_type":"code","colab":{}},"cell_type":"code","source":["#首先我们先来定义一个评分函数，这个评分函数能够帮助我们直接打印Xtrain上的交叉验证结果\n","def regassess(reg,Xtrain,Ytrain,cv,scoring = [\"r2\"],show=True):\n","    score = []\n","    for i in range(len(scoring)):\n","        if show:\n","            print(\"{}:{:.2f}\".format(scoring[i] #模型评估指标的名字\n","                                     ,CVS(reg\n","                                          ,Xtrain,Ytrain\n","                                          ,cv=cv,scoring=scoring[i]).mean()))\n","        score.append(CVS(reg,Xtrain,Ytrain,cv=cv,scoring=scoring[i]).mean())\n","    return score"],"execution_count":0,"outputs":[]},{"metadata":{"id":"ttJaD6BarDnW","colab_type":"code","colab":{}},"cell_type":"code","source":["reg = XGBR(n_estimators=180,random_state=420)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"pER34dU6rDna","colab_type":"code","outputId":"3d9d02d1-e599-4de4-a652-8b42d587d3a2","executionInfo":{"status":"ok","timestamp":1550300213376,"user_tz":-480,"elapsed":1645,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":71}},"cell_type":"code","source":["regassess(reg,Xtrain,Ytrain,cv,scoring = [\"r2\",\"neg_mean_squared_error\"])"],"execution_count":24,"outputs":[{"output_type":"stream","text":["r2:0.80\n","neg_mean_squared_error:-13.48\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["[0.8038787848970184, -13.482301822063182]"]},"metadata":{"tags":[]},"execution_count":24}]},{"metadata":{"id":"IiwOkzO5rDng","colab_type":"code","outputId":"f81cc8c4-bb47-4ccf-c32c-5230caa97968","executionInfo":{"status":"ok","timestamp":1550300216217,"user_tz":-480,"elapsed":1184,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"cell_type":"code","source":["regassess(reg,Xtrain,Ytrain,cv,scoring = [\"r2\",\"neg_mean_squared_error\"],show=False)"],"execution_count":25,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[0.8038787848970184, -13.482301822063182]"]},"metadata":{"tags":[]},"execution_count":25}]},{"metadata":{"id":"DK1AtqNbrDnl","colab_type":"code","outputId":"8bc5817c-1a1d-4b0d-f1dd-fd63e7dbbe68","executionInfo":{"status":"ok","timestamp":1550300225290,"user_tz":-480,"elapsed":4338,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":377}},"cell_type":"code","source":["from time import time\n","import datetime\n","\n","for i in [0,0.2,0.5,1]:\n","    time0=time()\n","    reg = XGBR(n_estimators=180,random_state=420,learning_rate=i)\n","    print(\"learning_rate = {}\".format(i))\n","    regassess(reg,Xtrain,Ytrain,cv,scoring = [\"r2\",\"neg_mean_squared_error\"])\n","    print(datetime.datetime.fromtimestamp(time()-time0).strftime(\"%M:%S:%f\"))\n","    print(\"\\t\")"],"execution_count":26,"outputs":[{"output_type":"stream","text":["learning_rate = 0\n","r2:-6.76\n","neg_mean_squared_error:-567.55\n","00:00:889485\n","\t\n","learning_rate = 0.2\n","r2:0.81\n","neg_mean_squared_error:-13.32\n","00:00:865498\n","\t\n","learning_rate = 0.5\n","r2:0.81\n","neg_mean_squared_error:-13.24\n","00:00:883588\n","\t\n","learning_rate = 1\n","r2:0.72\n","neg_mean_squared_error:-19.11\n","00:00:897876\n","\t\n"],"name":"stdout"}]},{"metadata":{"id":"CBEiZU2HrDno","colab_type":"code","outputId":"8b9f398e-f32a-4a39-f30b-f77eb00fc18c","executionInfo":{"status":"ok","timestamp":1550300241082,"user_tz":-480,"elapsed":10473,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":358}},"cell_type":"code","source":["axisx = np.arange(0.05,1,0.05)\n","rs = []\n","te = []\n","for i in axisx:\n","    reg = XGBR(n_estimators=180,random_state=420,learning_rate=i)\n","    score = regassess(reg,Xtrain,Ytrain,cv,scoring = [\"r2\",\"neg_mean_squared_error\"],show=False)\n","    test = reg.fit(Xtrain,Ytrain).score(Xtest,Ytest)\n","    rs.append(score[0])\n","    te.append(test)\n","print(axisx[rs.index(max(rs))],max(rs))\n","plt.figure(figsize=(20,5))\n","plt.plot(axisx,te,c=\"gray\",label=\"test\")\n","plt.plot(axisx,rs,c=\"green\",label=\"train\")\n","plt.legend()\n","plt.show()"],"execution_count":27,"outputs":[{"output_type":"stream","text":["0.55 0.8125604372670463\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABIUAAAEvCAYAAADb83GCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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tLT3Jr4BWCudCInGxBomSh6EQESXgJEy0dKOjI3C5nHC5nJiYGAcAqFQqlJbaYbc7kJubf8ur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\nfzTSWOmwAyLbAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 1440x360 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"metadata":{"id":"YUAX1tJZE3lD","colab_type":"text"},"cell_type":"markdown","source":["# 3 XGBoost的智慧"]},{"metadata":{"id":"wl0hLrherDnt","colab_type":"code","outputId":"c4c65fe1-12aa-41d4-bb42-808060b0267a","colab":{"base_uri":"https://localhost:8080/","height":125},"executionInfo":{"status":"ok","timestamp":1550300259712,"user_tz":-480,"elapsed":850,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["for booster in [\"gbtree\",\"gblinear\",\"dart\"]:\n","    reg = XGBR(n_estimators=180\n","               ,learning_rate=0.1\n","               ,random_state=420\n","               ,booster=booster).fit(Xtrain,Ytrain)\n","    print(booster)\n","    print(reg.score(Xtest,Ytest))"],"execution_count":28,"outputs":[{"output_type":"stream","text":["gbtree\n","0.9231068620728082\n","gblinear\n","0.6566081977043557\n","dart\n","0.923106843149575\n"],"name":"stdout"}]},{"metadata":{"id":"2OMXTo41rDnw","colab_type":"code","outputId":"9796a061-a658-4732-de7c-2d8e3f9d961e","colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"status":"ok","timestamp":1550300262045,"user_tz":-480,"elapsed":868,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["#默认reg:linear\n","reg = XGBR(n_estimators=180,random_state=420).fit(Xtrain,Ytrain)\n","reg.score(Xtest, Ytest)"],"execution_count":29,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.9231068620728082"]},"metadata":{"tags":[]},"execution_count":29}]},{"metadata":{"id":"atkLX-M9rDnz","colab_type":"code","outputId":"a9c35d79-eaea-4fc0-9965-ec551eaf2947","colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"status":"ok","timestamp":1550300265103,"user_tz":-480,"elapsed":812,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["MSE(Ytest,reg.predict(Xtest))"],"execution_count":30,"outputs":[{"output_type":"execute_result","data":{"text/plain":["7.155205217161047"]},"metadata":{"tags":[]},"execution_count":30}]},{"metadata":{"id":"l3F0wCUKrDn1","colab_type":"code","colab":{}},"cell_type":"code","source":["#xgb实现法\n","import xgboost as xgb"],"execution_count":0,"outputs":[]},{"metadata":{"id":"jYWGEn8frDn4","colab_type":"code","colab":{}},"cell_type":"code","source":["#使用类DMatrix读取数据\n","dtrain = xgb.DMatrix(Xtrain,Ytrain) #特征矩阵和标签都进行一个传入\n","dtest = xgb.DMatrix(Xtest,Ytest)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"_owqfm1rrDn5","colab_type":"code","outputId":"54f401ff-3376-4872-be30-99b9cb52625d","colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"status":"ok","timestamp":1550300274107,"user_tz":-480,"elapsed":851,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["#非常遗憾无法打开来查看，所以通常都是先读到pandas里面查看之后再放到DMatrix中\n","dtrain"],"execution_count":33,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<xgboost.core.DMatrix at 0x7fd519383c88>"]},"metadata":{"tags":[]},"execution_count":33}]},{"metadata":{"id":"WxEvK8rrrDn8","colab_type":"code","colab":{}},"cell_type":"code","source":["import pandas as pd"],"execution_count":0,"outputs":[]},{"metadata":{"id":"MuQSrnExrDn_","colab_type":"code","colab":{}},"cell_type":"code","source":["pd.DataFrame(Xtrain)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"YuWkGacHrDoB","colab_type":"code","colab":{}},"cell_type":"code","source":["#写明参数\n","param = {'silent':True #默认为False，通常要手动把它关闭掉\n","         ,'objective':'reg:linear'\n","         ,\"eta\":0.1}\n","num_round = 180 #n_estimators"],"execution_count":0,"outputs":[]},{"metadata":{"id":"it1akmTNrDoF","colab_type":"code","colab":{}},"cell_type":"code","source":["#类train，可以直接导入的参数是训练数据，树的数量，其他参数都需要通过params来导入\n","bst = xgb.train(param, dtrain, num_round)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"TBuBj0WIrDoH","colab_type":"code","colab":{}},"cell_type":"code","source":["#接口predict\n","preds = bst.predict(dtest)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"7uG8tqx_GUK2","colab_type":"text"},"cell_type":"markdown","source":["## 测试错误的数据格式"]},{"metadata":{"id":"NAxjYpLLGZUL","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":715},"outputId":"027ca222-4b3a-403d-fd84-9b5217b1fa07","executionInfo":{"status":"error","timestamp":1550300378579,"user_tz":-480,"elapsed":915,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["d1 = xgb.DMatrix(Xtrain)\n","bst1 = xgb.train(param, d1, num_round)"],"execution_count":39,"outputs":[{"output_type":"error","ename":"XGBoostError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mXGBoostError\u001b[0m                              Traceback (most recent call last)","\u001b[0;32m<ipython-input-39-8e67d1e825aa>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0md1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mxgb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDMatrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mXtrain\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mbst1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mxgb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparam\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_round\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/xgboost/training.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(params, dtrain, num_boost_round, evals, obj, feval, maximize, early_stopping_rounds, evals_result, verbose_eval, xgb_model, callbacks, learning_rates)\u001b[0m\n\u001b[1;32m    202\u001b[0m                            \u001b[0mevals\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mevals\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    203\u001b[0m                            \u001b[0mobj\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeval\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfeval\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 204\u001b[0;31m                            xgb_model=xgb_model, callbacks=callbacks)\n\u001b[0m\u001b[1;32m    205\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    206\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/xgboost/training.py\u001b[0m in \u001b[0;36m_train_internal\u001b[0;34m(params, dtrain, num_boost_round, evals, obj, feval, xgb_model, callbacks)\u001b[0m\n\u001b[1;32m     72\u001b[0m         \u001b[0;31m# Skip the first update if it is a recovery step.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     73\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mversion\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 74\u001b[0;31m             \u001b[0mbst\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     75\u001b[0m             \u001b[0mbst\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_rabit_checkpoint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     76\u001b[0m             \u001b[0mversion\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/xgboost/core.py\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, dtrain, iteration, fobj)\u001b[0m\n\u001b[1;32m    896\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mfobj\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    897\u001b[0m             _check_call(_LIB.XGBoosterUpdateOneIter(self.handle, ctypes.c_int(iteration),\n\u001b[0;32m--> 898\u001b[0;31m                                                     dtrain.handle))\n\u001b[0m\u001b[1;32m    899\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    900\u001b[0m             \u001b[0mpred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtrain\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/xgboost/core.py\u001b[0m in \u001b[0;36m_check_call\u001b[0;34m(ret)\u001b[0m\n\u001b[1;32m    128\u001b[0m     \"\"\"\n\u001b[1;32m    129\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mret\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 130\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mXGBoostError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_LIB\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mXGBGetLastError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    131\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mXGBoostError\u001b[0m: b'[06:59:43] src/objective/regression_obj.cc:100: Check failed: info.labels.size() != 0U (0 vs. 0) label set cannot be empty\\n\\nStack trace returned 10 entries:\\n[bt] (0) /usr/local/lib/python3.6/dist-packages/xgboost/./lib/libxgboost.so(_ZN4dmlc15LogMessageFatalD1Ev+0x3c) [0x7fd51e7477bc]\\n[bt] (1) /usr/local/lib/python3.6/dist-packages/xgboost/./lib/libxgboost.so(_ZN7xgboost3obj10RegLossObjINS0_16LinearSquareLossEE11GetGradientERKSt6vectorIfSaIfEERKNS_8MetaInfoEiPS4_INS_6detail18bst_gpair_internalIfEESaISE_EE+0x32e) [0x7fd51e7d50ce]\\n[bt] (2) /usr/local/lib/python3.6/dist-packages/xgboost/./lib/libxgboost.so(_ZN7xgboost11LearnerImpl13UpdateOneIterEiPNS_7DMatrixE+0x1ce) [0x7fd51e75207e]\\n[bt] (3) /usr/local/lib/python3.6/dist-packages/xgboost/./lib/libxgboost.so(XGBoosterUpdateOneIter+0x27) [0x7fd51e8b3817]\\n[bt] (4) /usr/lib/x86_64-linux-gnu/libffi.so.6(ffi_call_unix64+0x4c) [0x7fd545489dae]\\n[bt] (5) /usr/lib/x86_64-linux-gnu/libffi.so.6(ffi_call+0x22f) [0x7fd54548971f]\\n[bt] (6) /usr/lib/python3.6/lib-dynload/_ctypes.cpython-36m-x86_64-linux-gnu.so(_ctypes_callproc+0x2b4) [0x7fd54569d524]\\n[bt] (7) /usr/lib/python3.6/lib-dynload/_ctypes.cpython-36m-x86_64-linux-gnu.so(+0x11b93) [0x7fd54569db93]\\n[bt] (8) /usr/bin/python3(_PyObject_FastCallKeywords+0x19c) [0x5a730c]\\n[bt] (9) /usr/bin/python3() [0x503073]\\n'"]}]},{"metadata":{"id":"es3r822CrDoJ","colab_type":"code","outputId":"237871c2-96cb-43f0-a92e-e5729bdf38bc","colab":{"base_uri":"https://localhost:8080/","height":575},"executionInfo":{"status":"ok","timestamp":1550300396152,"user_tz":-480,"elapsed":820,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["preds"],"execution_count":40,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([ 6.4613175, 22.123888 , 30.755163 , 13.424351 ,  8.378565 ,\n","       23.608477 , 14.2151165, 16.026499 , 15.498961 , 14.10649  ,\n","       24.030867 , 34.36362  , 21.461111 , 28.839497 , 19.568035 ,\n","       10.188658 , 19.42369  , 23.539951 , 22.850523 , 23.198708 ,\n","       17.82486  , 16.07219  , 27.602034 , 20.773046 , 20.868807 ,\n","       15.865789 , 22.076588 , 29.292158 , 22.841051 , 15.770392 ,\n","       36.680496 , 21.057947 , 20.137005 , 23.777853 , 22.70615  ,\n","       23.863268 , 15.595315 , 24.565872 , 17.720552 , 33.95111  ,\n","       18.784286 , 20.483374 , 37.10668  , 18.068268 , 12.73839  ,\n","       31.186407 , 45.895035 , 12.696718 , 10.773068 , 36.064293 ,\n","       26.262571 , 19.908836 , 20.715096 , 48.814903 , 27.550056 ,\n","       25.225826 , 17.15366  , 21.215551 , 17.426773 , 18.478971 ,\n","       14.6453705, 22.841473 , 18.869593 , 29.990978 , 29.933191 ,\n","       18.756853 , 18.784918 , 16.33361  , 23.155968 , 19.144344 ,\n","       29.724382 , 42.121906 , 31.544363 , 23.017508 , 19.536028 ,\n","       23.851992 , 41.790577 , 28.676506 , 20.036425 , 21.723856 ,\n","       19.537868 , 46.349495 , 23.119637 ,  8.071444 , 26.358177 ,\n","       24.85706  , 17.057547 , 20.084204 , 18.54005  ,  7.157663 ,\n","       20.593962 , 15.451031 , 45.09552  , 34.435097 , 22.969654 ,\n","       10.10335  , 10.803318 , 18.42058  ,  7.800361 , 11.79309  ,\n","       30.755335 , 10.80648  , 26.122625 , 22.589502 , 31.219454 ,\n","       42.283318 , 19.274109 ,  7.3861685, 23.055706 , 14.315018 ,\n","       45.136368 , 21.243176 , 19.715647 , 24.533583 , 18.24247  ,\n","       28.382742 , 23.41182  , 19.962458 , 45.916683 , 17.521889 ,\n","       24.13039  , 26.147182 , 18.418781 , 17.606575 , 14.540631 ,\n","       20.595512 , 32.59128  , 10.155618 , 20.53032  , 21.477484 ,\n","       17.450048 , 20.154486 ,  8.010227 , 30.482618 , 29.677181 ,\n","       20.357098 , 18.222181 , 14.14504  , 10.100547 , 18.85027  ,\n","       41.85804  , 17.44544  , 22.907183 , 21.02398  , 29.799366 ,\n","       20.219465 , 12.404763 , 45.750965 , 25.56757  , 22.000706 ,\n","       14.194921 , 27.102774 ], dtype=float32)"]},"metadata":{"tags":[]},"execution_count":40}]},{"metadata":{"id":"yANlWKSHrDoN","colab_type":"code","outputId":"7fd2bde5-4c21-4054-b0bb-8c37f772f424","colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"status":"ok","timestamp":1550300405175,"user_tz":-480,"elapsed":880,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["from sklearn.metrics import r2_score\n","r2_score(Ytest,preds)"],"execution_count":41,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.9260984298390122"]},"metadata":{"tags":[]},"execution_count":41}]},{"metadata":{"id":"24fBAtsHrDoQ","colab_type":"code","outputId":"6b8563ea-2dfc-40d0-ef49-4b1406fb0d27","colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"status":"ok","timestamp":1550300406788,"user_tz":-480,"elapsed":613,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["MSE(Ytest,preds)"],"execution_count":42,"outputs":[{"output_type":"execute_result","data":{"text/plain":["6.87682821415069"]},"metadata":{"tags":[]},"execution_count":42}]},{"metadata":{"id":"mef91sgpG5HR","colab_type":"text"},"cell_type":"markdown","source":["##  让树停止生长：重要参数gamma"]},{"metadata":{"id":"Us5FWRdgrDoS","colab_type":"code","colab":{}},"cell_type":"code","source":["import xgboost as xgb\n","\n","#为了便捷，使用全数据\n","dfull = xgb.DMatrix(X,y)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"bUM994WRrDoU","colab_type":"code","colab":{}},"cell_type":"code","source":["#设定参数\n","param1 = {'silent':True,'obj':'reg:linear',\"gamma\":0}\n","num_round = 100\n","n_fold=5 #sklearn - KFold"],"execution_count":0,"outputs":[]},{"metadata":{"id":"p7lw-AvArDoW","colab_type":"code","outputId":"49464290-c236-44c9-9528-120b209409c1","colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"status":"ok","timestamp":1550300469336,"user_tz":-480,"elapsed":964,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["#使用类xgb.cv\n","time0 = time()\n","cvresult1 = xgb.cv(param1, dfull, num_round,n_fold)\n","print(datetime.datetime.fromtimestamp(time()-time0).strftime(\"%M:%S:%f\"))"],"execution_count":45,"outputs":[{"output_type":"stream","text":["00:00:347619\n"],"name":"stdout"}]},{"metadata":{"id":"7QQOUffMrDob","colab_type":"code","outputId":"7bd431e5-aeac-4189-b767-ea68f3017ff4","colab":{"base_uri":"https://localhost:8080/","height":1969},"executionInfo":{"status":"ok","timestamp":1550300472602,"user_tz":-480,"elapsed":824,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["#看看类xgb.cv生成了什么结果？\n","cvresult1 #随着树不断增加，我们的模型的效果如何变化"],"execution_count":46,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>test-rmse-mean</th>\n","      <th>test-rmse-std</th>\n","      <th>train-rmse-mean</th>\n","      <th>train-rmse-std</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>17.163215</td>\n","      <td>0.584296</td>\n","      <td>17.105578</td>\n","      <td>0.129116</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>12.519736</td>\n","      <td>0.473458</td>\n","      <td>12.337973</td>\n","      <td>0.097557</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>9.404534</td>\n","      <td>0.472310</td>\n","      <td>8.994071</td>\n","      <td>0.065756</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>7.250335</td>\n","      <td>0.500342</td>\n","      <td>6.629481</td>\n","      <td>0.050323</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5.920812</td>\n","      <td>0.591874</td>\n","      <td>4.954406</td>\n","      <td>0.033209</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>5.045190</td>\n","      <td>0.687971</td>\n","      <td>3.781454</td>\n","      <td>0.029604</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>4.472030</td>\n","      <td>0.686492</td>\n","      <td>2.947767</td>\n","      <td>0.038786</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>4.179314</td>\n","      <td>0.737935</td>\n","      <td>2.357748</td>\n","      <td>0.042040</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>3.979878</td>\n","      <td>0.798198</td>\n","      <td>1.951907</td>\n","      <td>0.044972</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>3.870751</td>\n","      <td>0.812331</td>\n","      <td>1.660895</td>\n","      <td>0.044894</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>3.816196</td>\n","      <td>0.835251</td>\n","      <td>1.464296</td>\n","      <td>0.049422</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>3.788125</td>\n","      <td>0.841643</td>\n","      <td>1.323362</td>\n","      <td>0.056240</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>3.766973</td>\n","      <td>0.848989</td>\n","      <td>1.214468</td>\n","      <td>0.046524</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>3.741199</td>\n","      <td>0.872370</td>\n","      <td>1.137311</td>\n","      <td>0.044522</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>3.729194</td>\n","      <td>0.879429</td>\n","      <td>1.064629</td>\n","      <td>0.042245</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>3.717997</td>\n","      <td>0.879572</td>\n","      <td>1.010286</td>\n","      <td>0.038892</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>3.706736</td>\n","      <td>0.878032</td>\n","      <td>0.941258</td>\n","      <td>0.038360</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>3.693886</td>\n","      <td>0.873913</td>\n","      <td>0.883599</td>\n","      <td>0.056640</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>3.693296</td>\n","      <td>0.883429</td>\n","      <td>0.829674</td>\n","      <td>0.057284</td>\n","    </tr>\n","    <tr>\n","      <th>19</th>\n","      <td>3.687510</td>\n","      <td>0.880928</td>\n","      <td>0.772332</td>\n","      <td>0.042899</td>\n","    </tr>\n","    <tr>\n","      <th>20</th>\n","      <td>3.687037</td>\n","      <td>0.879180</td>\n","      <td>0.731557</td>\n","      <td>0.049150</td>\n","    </tr>\n","    <tr>\n","      <th>21</th>\n","      <td>3.677507</td>\n","      <td>0.882060</td>\n","      <td>0.690698</td>\n","      <td>0.041190</td>\n","    </tr>\n","    <tr>\n","      <th>22</th>\n","      <td>3.675343</td>\n","      <td>0.883635</td>\n","      <td>0.657743</td>\n","      <td>0.042137</td>\n","    </tr>\n","    <tr>\n","      <th>23</th>\n","      <td>3.671006</td>\n","      <td>0.879224</td>\n","      <td>0.619988</td>\n","      <td>0.054097</td>\n","    </tr>\n","    <tr>\n","      <th>24</th>\n","      <td>3.670951</td>\n","      <td>0.867470</td>\n","      <td>0.585414</td>\n","      <td>0.052585</td>\n","    </tr>\n","    <tr>\n","      <th>25</th>\n","      <td>3.673598</td>\n","      <td>0.863241</td>\n","      <td>0.548723</td>\n","      <td>0.054440</td>\n","    </tr>\n","    <tr>\n","      <th>26</th>\n","      <td>3.673988</td>\n","      <td>0.867116</td>\n","      <td>0.527266</td>\n","      <td>0.049630</td>\n","    </tr>\n","    <tr>\n","      <th>27</th>\n","      <td>3.671702</td>\n","      <td>0.864566</td>\n","      <td>0.504405</td>\n","      <td>0.040376</td>\n","    </tr>\n","    <tr>\n","      <th>28</th>\n","      <td>3.671324</td>\n","      <td>0.862536</td>\n","      <td>0.468534</td>\n","      <td>0.033020</td>\n","    </tr>\n","    <tr>\n","      <th>29</th>\n","      <td>3.675074</td>\n","      <td>0.864713</td>\n","      <td>0.448633</td>\n","      <td>0.032191</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>70</th>\n","      <td>3.668067</td>\n","      <td>0.859435</td>\n","      <td>0.071057</td>\n","      <td>0.015411</td>\n","    </tr>\n","    <tr>\n","      <th>71</th>\n","      <td>3.667708</td>\n","      <td>0.859370</td>\n","      <td>0.067946</td>\n","      <td>0.013960</td>\n","    </tr>\n","    <tr>\n","      <th>72</th>\n","      <td>3.668174</td>\n","      <td>0.859307</td>\n","      <td>0.065197</td>\n","      <td>0.012475</td>\n","    </tr>\n","    <tr>\n","      <th>73</th>\n","      <td>3.668738</td>\n","      <td>0.859471</td>\n","      <td>0.062789</td>\n","      <td>0.012538</td>\n","    </tr>\n","    <tr>\n","      <th>74</th>\n","      <td>3.668950</td>\n","      <td>0.860112</td>\n","      <td>0.060294</td>\n","      <td>0.012669</td>\n","    </tr>\n","    <tr>\n","      <th>75</th>\n","      <td>3.669084</td>\n","      <td>0.859966</td>\n","      <td>0.058278</td>\n","      <td>0.012055</td>\n","    </tr>\n","    <tr>\n","      <th>76</th>\n","      <td>3.669627</td>\n","      <td>0.859505</td>\n","      <td>0.055402</td>\n","      <td>0.011065</td>\n","    </tr>\n","    <tr>\n","      <th>77</th>\n","      <td>3.669904</td>\n","      <td>0.859294</td>\n","      <td>0.053819</td>\n","      <td>0.011072</td>\n","    </tr>\n","    <tr>\n","      <th>78</th>\n","      <td>3.670185</td>\n","      <td>0.859204</td>\n","      <td>0.051280</td>\n","      <td>0.011215</td>\n","    </tr>\n","    <tr>\n","      <th>79</th>\n","      <td>3.670092</td>\n","      <td>0.859250</td>\n","      <td>0.048748</td>\n","      <td>0.009988</td>\n","    </tr>\n","    <tr>\n","      <th>80</th>\n","      <td>3.669869</td>\n","      <td>0.858892</td>\n","      <td>0.046972</td>\n","      <td>0.009233</td>\n","    </tr>\n","    <tr>\n","      <th>81</th>\n","      <td>3.669702</td>\n","      <td>0.858676</td>\n","      <td>0.044753</td>\n","      <td>0.008664</td>\n","    </tr>\n","    <tr>\n","      <th>82</th>\n","      <td>3.669704</td>\n","      <td>0.858921</td>\n","      <td>0.043148</td>\n","      <td>0.008636</td>\n","    </tr>\n","    <tr>\n","      <th>83</th>\n","      <td>3.669596</td>\n","      <td>0.858843</td>\n","      <td>0.041823</td>\n","      <td>0.008355</td>\n","    </tr>\n","    <tr>\n","      <th>84</th>\n","      <td>3.669730</td>\n","      <td>0.858459</td>\n","      <td>0.040257</td>\n","      <td>0.008378</td>\n","    </tr>\n","    <tr>\n","      <th>85</th>\n","      <td>3.669835</td>\n","      <td>0.858698</td>\n","      <td>0.038518</td>\n","      <td>0.007731</td>\n","    </tr>\n","    <tr>\n","      <th>86</th>\n","      <td>3.669705</td>\n","      <td>0.858958</td>\n","      <td>0.036694</td>\n","      <td>0.006928</td>\n","    </tr>\n","    <tr>\n","      <th>87</th>\n","      <td>3.669722</td>\n","      <td>0.858715</td>\n","      <td>0.034932</td>\n","      <td>0.006174</td>\n","    </tr>\n","    <tr>\n","      <th>88</th>\n","      <td>3.669964</td>\n","      <td>0.858547</td>\n","      <td>0.033947</td>\n","      <td>0.006206</td>\n","    </tr>\n","    <tr>\n","      <th>89</th>\n","      <td>3.669988</td>\n","      <td>0.858516</td>\n","      <td>0.032706</td>\n","      <td>0.006176</td>\n","    </tr>\n","    <tr>\n","      <th>90</th>\n","      <td>3.670116</td>\n","      <td>0.858512</td>\n","      <td>0.031317</td>\n","      <td>0.006171</td>\n","    </tr>\n","    <tr>\n","      <th>91</th>\n","      <td>3.669930</td>\n","      <td>0.858759</td>\n","      <td>0.029697</td>\n","      <td>0.005473</td>\n","    </tr>\n","    <tr>\n","      <th>92</th>\n","      <td>3.669906</td>\n","      <td>0.858549</td>\n","      <td>0.028561</td>\n","      <td>0.005599</td>\n","    </tr>\n","    <tr>\n","      <th>93</th>\n","      <td>3.669822</td>\n","      <td>0.858554</td>\n","      <td>0.027585</td>\n","      <td>0.005694</td>\n","    </tr>\n","    <tr>\n","      <th>94</th>\n","      <td>3.669985</td>\n","      <td>0.858390</td>\n","      <td>0.026436</td>\n","      <td>0.005414</td>\n","    </tr>\n","    <tr>\n","      <th>95</th>\n","      <td>3.669921</td>\n","      <td>0.858313</td>\n","      <td>0.025204</td>\n","      <td>0.005145</td>\n","    </tr>\n","    <tr>\n","      <th>96</th>\n","      <td>3.669983</td>\n","      <td>0.858255</td>\n","      <td>0.024422</td>\n","      <td>0.005242</td>\n","    </tr>\n","    <tr>\n","      <th>97</th>\n","      <td>3.669947</td>\n","      <td>0.858331</td>\n","      <td>0.023661</td>\n","      <td>0.005117</td>\n","    </tr>\n","    <tr>\n","      <th>98</th>\n","      <td>3.669868</td>\n","      <td>0.858578</td>\n","      <td>0.022562</td>\n","      <td>0.004704</td>\n","    </tr>\n","    <tr>\n","      <th>99</th>\n","      <td>3.669824</td>\n","      <td>0.858305</td>\n","      <td>0.021496</td>\n","      <td>0.004738</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>100 rows × 4 columns</p>\n","</div>"],"text/plain":["    test-rmse-mean  test-rmse-std  train-rmse-mean  train-rmse-std\n","0        17.163215       0.584296        17.105578        0.129116\n","1        12.519736       0.473458        12.337973        0.097557\n","2         9.404534       0.472310         8.994071        0.065756\n","3         7.250335       0.500342         6.629481        0.050323\n","4         5.920812       0.591874         4.954406        0.033209\n","5         5.045190       0.687971         3.781454        0.029604\n","6         4.472030       0.686492         2.947767        0.038786\n","7         4.179314       0.737935         2.357748        0.042040\n","8         3.979878       0.798198         1.951907        0.044972\n","9         3.870751       0.812331         1.660895        0.044894\n","10        3.816196       0.835251         1.464296        0.049422\n","11        3.788125       0.841643         1.323362        0.056240\n","12        3.766973       0.848989         1.214468        0.046524\n","13        3.741199       0.872370         1.137311        0.044522\n","14        3.729194       0.879429         1.064629        0.042245\n","15        3.717997       0.879572         1.010286        0.038892\n","16        3.706736       0.878032         0.941258        0.038360\n","17        3.693886       0.873913         0.883599        0.056640\n","18        3.693296       0.883429         0.829674        0.057284\n","19        3.687510       0.880928         0.772332        0.042899\n","20        3.687037       0.879180         0.731557        0.049150\n","21        3.677507       0.882060         0.690698        0.041190\n","22        3.675343       0.883635         0.657743        0.042137\n","23        3.671006       0.879224         0.619988        0.054097\n","24        3.670951       0.867470         0.585414        0.052585\n","25        3.673598       0.863241         0.548723        0.054440\n","26        3.673988       0.867116         0.527266        0.049630\n","27        3.671702       0.864566         0.504405        0.040376\n","28        3.671324       0.862536         0.468534        0.033020\n","29        3.675074       0.864713         0.448633        0.032191\n","..             ...            ...              ...             ...\n","70        3.668067       0.859435         0.071057        0.015411\n","71        3.667708       0.859370         0.067946        0.013960\n","72        3.668174       0.859307         0.065197        0.012475\n","73        3.668738       0.859471         0.062789        0.012538\n","74        3.668950       0.860112         0.060294        0.012669\n","75        3.669084       0.859966         0.058278        0.012055\n","76        3.669627       0.859505         0.055402        0.011065\n","77        3.669904       0.859294         0.053819        0.011072\n","78        3.670185       0.859204         0.051280        0.011215\n","79        3.670092       0.859250         0.048748        0.009988\n","80        3.669869       0.858892         0.046972        0.009233\n","81        3.669702       0.858676         0.044753        0.008664\n","82        3.669704       0.858921         0.043148        0.008636\n","83        3.669596       0.858843         0.041823        0.008355\n","84        3.669730       0.858459         0.040257        0.008378\n","85        3.669835       0.858698         0.038518        0.007731\n","86        3.669705       0.858958         0.036694        0.006928\n","87        3.669722       0.858715         0.034932        0.006174\n","88        3.669964       0.858547         0.033947        0.006206\n","89        3.669988       0.858516         0.032706        0.006176\n","90        3.670116       0.858512         0.031317        0.006171\n","91        3.669930       0.858759         0.029697        0.005473\n","92        3.669906       0.858549         0.028561        0.005599\n","93        3.669822       0.858554         0.027585        0.005694\n","94        3.669985       0.858390         0.026436        0.005414\n","95        3.669921       0.858313         0.025204        0.005145\n","96        3.669983       0.858255         0.024422        0.005242\n","97        3.669947       0.858331         0.023661        0.005117\n","98        3.669868       0.858578         0.022562        0.004704\n","99        3.669824       0.858305         0.021496        0.004738\n","\n","[100 rows x 4 columns]"]},"metadata":{"tags":[]},"execution_count":46}]},{"metadata":{"id":"beRKTMLarDod","colab_type":"code","outputId":"394035c3-dcf3-4192-f6b0-b0cc8ee84663","colab":{"base_uri":"https://localhost:8080/","height":340},"executionInfo":{"status":"ok","timestamp":1550300479365,"user_tz":-480,"elapsed":854,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["plt.figure(figsize=(20,5))\n","plt.grid()\n","plt.plot(range(1,101),cvresult1.iloc[:,0],c=\"red\",label=\"train,gamma=0\")\n","plt.plot(range(1,101),cvresult1.iloc[:,2],c=\"orange\",label=\"test,gamma=0\")\n","plt.legend()\n","plt.show()\n","\n","#从这个图中，我们可以看出什么？\n","#怎样从图中观察模型的泛化能力？\n","#从这个图的角度来说，模型的调参目标是什么？"],"execution_count":47,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABIUAAAEvCAYAAADb83GCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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size 1440x360 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"metadata":{"id":"tXfO0T1qrDog","colab_type":"code","colab":{}},"cell_type":"code","source":["#xgboost中回归模型的默认模型评估指标是什么？"],"execution_count":0,"outputs":[]},{"metadata":{"id":"DfoDXgaKrDoi","colab_type":"code","outputId":"59d0e906-11f4-4f04-a09f-295c7dbdaab7","colab":{"base_uri":"https://localhost:8080/","height":340},"executionInfo":{"status":"ok","timestamp":1550300515331,"user_tz":-480,"elapsed":2251,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["param1 = {'silent':True,'obj':'reg:linear',\"gamma\":0,\"eval_metric\":\"mae\"}\n","cvresult1 = xgb.cv(param1, dfull, num_round,n_fold)\n","\n","plt.figure(figsize=(20,5))\n","plt.grid()\n","plt.plot(range(1,101),cvresult1.iloc[:,0],c=\"red\",label=\"train,gamma=0\")\n","plt.plot(range(1,101),cvresult1.iloc[:,2],c=\"orange\",label=\"test,gamma=0\")\n","plt.legend()\n","plt.show()"],"execution_count":49,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABHwAAAEvCAYAAAA3qtbRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzs3Xl8VPWh/vHnnFmSTBLIjrIIFBGX\nQq1KS9iKinaxLtWrbalYa2sV61bbn9ttaytuWNvrUlsr1qVSblHqQq9eqbhUriyCKBWqLAooASGE\n7MlklnN+f8ySSTIJWSY5yeTzfr3yysxZvuc5kwnow3fOMWzbtgUAAAAAAIC0YTodAAAAAAAAAKlF\n4QMAAAAAAJBmKHwAAAAAAADSDIUPAAAAAABAmqHwAQAAAAAASDMUPgAAAAAAAGnG3RcHKS+v7YvD\nAAAAAAAADBrFxbntrmOGDwAAAAAAQJrpVOGzdetWzZ49W4sWLZIkBYNB/eQnP9F//Md/6Lvf/a6q\nq6t7NSQAAAAAAAA675CFT0NDg+bPn6/S0tL4sqeeekr5+flaunSpvva1r2n9+vW9GhIAAAAAAACd\nd8jCx+v1auHChSopKYkve+2113TWWWdJkr75zW/q1FNP7b2EAAAAAAAA6JJDFj5ut1uZmZktlpWV\nlemNN97Q3Llz9eMf/1hVVVW9FhAAAAAAAABd062LNtu2rbFjx+rJJ5/U+PHj9cc//jHVuQAAAAAA\nANBN3Sp8ioqKNHnyZEnS9OnTtX379pSGAgAAAAAAQPd1q/CZOXOmVq5cKUnavHmzxo4dm9JQAAAA\nAAAA6D7Dtm27ow02bdqkBQsWqKysTG63W8OGDdM999yj22+/XeXl5fL5fFqwYIGKioraHaO8vDbl\nwQEAAAAAQHp6/fVXNGvWoW8Qdd99v9H5539Lw4eP6INUvef++3+jzZs3yTAMXXPNT3TMMcd1ar/i\n4tx21x2y8EkFCh8AAAAAANAZe/fu0YMP3qvbbrvb6Sh94p133tZ///eTuvvue7Vz5w7deeet+uMf\nH+vUvh0VPu5UBRwMXLWb5a7/QE2Hned0FAAAAAAA0tJvf7tA77+/WTNmTNbpp39Ve/fu0b33/l53\n3nmrysv3q7GxUZdc8kNNmzZDV175Q1133fV67bVXVF9fp48/3qWyst26+uqfqLR0WnzMUCikW2/9\nuT79dK8mTpykV19doWeffVHr1q3VI488JI/Ho9zcXN166116772Nevrpv8rlcmnr1g900UWXaO3a\n1dq2bYuuuOIazZw5SxdccLamT5+p9evf0pQpU2VZttatW6spU6Zq3ryrko67bt1aLV785xbnetZZ\n5+rjj3dqxoxZkqQxY8aqtrZG9fV1ys7O6dHrSOHTBTlv/kRe1yodyP+S7Iz2P8IGAAAAAAC659vf\nnqtnnnlKY8eO08cf79Tvf/+IKisP6gtfmKKvfvXrKivbrZ///EZNmzajxX779+/TPffcrzVrVun5\n5//WovBZs2aVAoEmPfzw43rzzZV66qn/liTV1tbqlltu0/DhIzR//i+0du1q+Xw+bd++VX/5y1Jt\n3LhBv/rVz/X008u0efN7+tvflmjmzFnau3ePzj77PP3whz/S1752ih544GFdeunlOu+8MzVv3lVJ\nx50+faamTp3e5nwXLLhdEyYcHX+el5eviooKCp++ZH5YIR0leXatVuCoM52OAwAAAABAr8r+5c+U\n8ffnUjpm05nnqP6Xt3Vq29i1bHJzh+j99zdr2bJnZBimamqq22w7adLxkqSSkhLV1dW1WLdr1w5N\nnPg5SVJp6TS5XC5JUl5enhYsuE3hcFh79pTpxBMny+fz6cgjx8vr9aqwsEijRh2hrKwsFRQUxMfN\nzs7W6NFjJElZWVmaMOFoud1u2bbV7ridlaor71D4dEE4Y7Tc2iJ32XoKHwAAAAAAepnH45Ekvfzy\nS6qpqdGDDz6impoa/eAHc9tsGytxpLaliW3bMs3IesMwZBiGJOnOO+fr17++V2PGjNVvf7sg6VjJ\nxk1cJklud8t6Jdm4q1b9X9KPdBUVFamioiK+7MCBAx3eGKuzKHy6IJR/rDL0D7kr/+10FAAAAAAA\nel39L2/r9GycVDFNU+FwuMWyqqoqHX74cJmmqX/+81UFg8EujTlixEi9/vorkqS33loTH7++vk7D\nhh2m2tpabdjwtsaNG5+Sc0g27tSp05N+pOu99zbqT3/6o8455zxt2fKBioqK5PNl9ziD2eMRBpHQ\nyJMkSa7GHQ4nAQAAAAAgPY0ePVZbtnyg+vrmj2XNmnWKVq1aqWuumaesrCyVlJTosccWHnKs++77\njfbsKdPUqTNUX1+vefO+r40b39GQIUMlSeeee77mzfu+7r77dn3nOxdp0aLHVVFxoMfnkGzcAweS\njztx4uc0YcIxuvzyS3Tvvb/Wddfd0OPjS9yWvUuMA+UqWjtOVs1QVXz7E6fjAAAAAACADjzzzNMq\nLZ2m7OxsbdiwXrNmnary8v265pp5Wrz4b07H6zFuy54idmGR7ApTRl56FFgAAAAAAKSzkpJhOvzw\n4QqFQnr11RVavPhJ2balq666zulovY4ZPl1U+IdRMo+s1oEZH8rOLHY6DgAAAAAAGKQ6muHDNXy6\nyLJKJEmej9c6nAQAAAAAACA5Cp8uCmeOlSS5d69zOAkAAAAAAEByFD5dFCo4VpLkrtzscBIAAAAA\nAIDkKHy6KDRisiTJ1bjT2SAAAAAAAADtoPDpouCRU6SQZJr7nI4CAAAAAEBaev31V7q0/bvvblBl\n5cFeStM77r//N7rssu/p8ssv0fvvp/5TRBQ+XWQXFMs+YMrITp87jwEAAAAA0F/s3btHK1Ys79I+\nL7ywbEAVPu+887Z27/5Ef/zjY7rxxp/r3nvvSfkx3CkfcRCw63NlHlYtw39AdmaR03EAAAAAAEgb\nv/3tAr3//mY9+ujD+uij7aqtrVU4HNa11/4/HXnkeC1a9Lj++c/XZJqmpk2boWOOOVYrV76uHTs+\n0m233a3DDjtMkhQKhXTrrT/Xp5/u1cSJk/Tqqyv07LMvat26tXrkkYfk8XiUm5urW2+9S++9t1FP\nP/1XuVwubd36gS666BKtXbta27Zt0RVXXKOZM2fpggvO1vTpM7V+/VuaMmWqLMvWunVrNWXKVM2b\nd1XScdetW6vFi//c4vzOOutcffzxTs2YMUuSNGbMWNXW1qi+vk7Z2Tkpex0pfLrBskpkqlqenasV\nOPpMp+MAAAAAAJA2vv3tuXrmmadkmqa++MWpOvPMc7Rjx0e67757dO+9v9df/7pIzz33klwul557\n7m+aPHmKjjzyKF133fXxskeS1qxZpUCgSQ8//LjefHOlnnrqvyVJtbW1uuWW2zR8+AjNn/8LrV27\nWj6fT9u3b9Vf/rJUGzdu0K9+9XM9/fQybd78nv72tyWaOXOW9u7do7PPPk8//OGP9LWvnaIHHnhY\nl156uc4770zNm3dV0nGnT5+pqVOntznHBQtu14QJR8ef5+Xlq6KigsLHaeGssXJrm9xl6yl8AAAA\nAABpK3vrz5Sx77mUjtk07BzVH3XbIbd7771/qaqqUsuXvxjZr8kvSZo161Rde+0VOu20r+j007/S\n7v67du3QxImfkySVlk6Ty+WSJOXl5WnBgtsUDoe1Z0+ZTjxxsnw+n448cry8Xq8KC4s0atQRysrK\nUkFBgerq6iRJ2dnZGj16jCQpKytLEyYcLbfbLdu22h23s2zb7vS2nUXh0w2h/OOUoX/IfZBbswMA\nAAAA0Bs8Hrd+/OP/p89+dlKL5T/96U3atWunXn31ZV111WV6+OEnku5v27ZMM1LyGIYhwzAkSXfe\nOV+//vW9GjNmrH772wXx7WOFUOvHsTImcZkkud0tK5Vk465a9X9JP9JVVFSkioqK+LIDBw6oqCi1\nl4yh8OmG0IgTpSrJ1bTT6SgAAAAAAPSa+qNu69RsnFQyTVPhcFjHHvtZvfHG6/rsZydpx46PtHbt\nKn396+fo6af/W9/73qX63vcu1bvvvqOGhvr4PolGjBgZv9vXW2+tia+vr6/TsGGHqba2Vhs2vK1x\n48anJHeycadOnZ70I13vvbdRf/rTH3XOOedpy5YPVFRUJJ8vOyU5Yih8uiF4ZKm0RjINbs0OAAAA\nAEAqjR49Vlu2fKDDDx+uffs+1RVX/ECWZenaa3+qnJwcVVVV6tJLL1JWlk+f/ewkDRkyVMcff4J+\n9rMbdOedv9Hf//6czj//W5o6dYZeeGGZ5s37vj7/+RM1ZMhQSdK5556vefO+r1GjjtB3vnORHn30\nYf3wh1f0OHeycadNm5l05s7EiZ/ThAnH6PLLL5FhGLruuht6fPzWDLs3PijWSnl5+t3CvGhRnpQj\nHTinyukoAAAAAAAg6plnnlZp6TRlZ2drw4b1mjXrVJWX79c118zT4sV/czpeShUX57a7jhk+3WTX\n58ocVi2jsUJ2VqHTcQAAAAAAgKSSkmE6/PDhCoVCevXVFVq8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size 1440x360 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"metadata":{"id":"XAPKUN-hrDoo","colab_type":"code","outputId":"b5fa60de-a3cc-4bb6-8f75-8ffa6922caf7","colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"status":"ok","timestamp":1550300524704,"user_tz":-480,"elapsed":2704,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["param1 = {'silent':True,'obj':'reg:linear',\"gamma\":0}\n","param2 = {'silent':True,'obj':'reg:linear',\"gamma\":20}\n","num_round = 180\n","n_fold=5\n","\n","time0 = time()\n","cvresult1 = xgb.cv(param1, dfull, num_round,n_fold)\n","print(datetime.datetime.fromtimestamp(time()-time0).strftime(\"%M:%S:%f\"))"],"execution_count":50,"outputs":[{"output_type":"stream","text":["00:00:596644\n"],"name":"stdout"}]},{"metadata":{"id":"LpXopOXrrDot","colab_type":"code","outputId":"d88ac4ca-3c1b-4621-ec0d-31ddef277209","colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"status":"ok","timestamp":1550300529290,"user_tz":-480,"elapsed":1231,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["time0 = time()\n","cvresult2 = xgb.cv(param2, dfull, num_round,n_fold)\n","print(datetime.datetime.fromtimestamp(time()-time0).strftime(\"%M:%S:%f\"))"],"execution_count":51,"outputs":[{"output_type":"stream","text":["00:00:610645\n"],"name":"stdout"}]},{"metadata":{"id":"UYfHQya0rDow","colab_type":"code","outputId":"8569b566-fad4-491d-fff8-c07710e304c5","colab":{"base_uri":"https://localhost:8080/","height":340},"executionInfo":{"status":"ok","timestamp":1550300535468,"user_tz":-480,"elapsed":1334,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["plt.figure(figsize=(20,5))\n","plt.grid()\n","plt.plot(range(1,181),cvresult1.iloc[:,0],c=\"red\",label=\"train,gamma=0\")\n","plt.plot(range(1,181),cvresult1.iloc[:,2],c=\"orange\",label=\"test,gamma=0\")\n","plt.plot(range(1,181),cvresult2.iloc[:,0],c=\"green\",label=\"train,gamma=20\")\n","plt.plot(range(1,181),cvresult2.iloc[:,2],c=\"blue\",label=\"test,gamma=20\")\n","plt.legend()\n","plt.show()\n","\n","#从这里，你看出gamma是如何控制过拟合了吗？控制训练集上的训练 - 降低训练集上的表现"],"execution_count":52,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABIUAAAEvCAYAAADb83GCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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size 1440x360 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"metadata":{"id":"8Z-qYmZXHS-E","colab_type":"text"},"cell_type":"markdown","source":["试一个分类的例子"]},{"metadata":{"id":"nA0CsEKDrDo0","colab_type":"code","colab":{}},"cell_type":"code","source":["from sklearn.datasets import load_breast_cancer\n","data2 = load_breast_cancer()\n","\n","x2 = data2.data\n","y2 = data2.target\n","\n","dfull2 = xgb.DMatrix(x2,y2)\n","\n","param1 = {'silent':True,'obj':'binary:logistic',\"gamma\":0,\"nfold\":5}\n","param2 = {'silent':True,'obj':'binary:logistic',\"gamma\":1,\"nfold\":5}\n","num_round = 100"],"execution_count":0,"outputs":[]},{"metadata":{"id":"dR__nBG4rDo3","colab_type":"code","outputId":"8561a065-8ebd-4693-bf68-c6d9d01da99d","colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"status":"ok","timestamp":1550300566288,"user_tz":-480,"elapsed":866,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["time0 = time()\n","cvresult1 = xgb.cv(param1, dfull2, num_round)\n","print(datetime.datetime.fromtimestamp(time()-time0).strftime(\"%M:%S:%f\"))"],"execution_count":54,"outputs":[{"output_type":"stream","text":["00:00:276067\n"],"name":"stdout"}]},{"metadata":{"id":"6-RCR0EOrDo8","colab_type":"code","outputId":"08c52e48-a31f-42da-feed-548702572a8a","colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"status":"ok","timestamp":1550300568544,"user_tz":-480,"elapsed":1276,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["time0 = time()\n","cvresult2 = xgb.cv(param2, dfull2, num_round) \n","print(datetime.datetime.fromtimestamp(time()-time0).strftime(\"%M:%S:%f\"))"],"execution_count":55,"outputs":[{"output_type":"stream","text":["00:00:473792\n"],"name":"stdout"}]},{"metadata":{"id":"pTIMvmkprDo-","colab_type":"code","outputId":"6bb2ccb5-f5a8-4410-f12c-cee67fb5fa08","colab":{"base_uri":"https://localhost:8080/","height":343},"executionInfo":{"status":"ok","timestamp":1550300570805,"user_tz":-480,"elapsed":978,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["plt.figure(figsize=(20,5))\n","plt.grid()\n","plt.plot(range(1,101),cvresult1.iloc[:,0],c=\"red\",label=\"train,gamma=0\")\n","plt.plot(range(1,101),cvresult1.iloc[:,2],c=\"orange\",label=\"test,gamma=0\")\n","plt.plot(range(1,101),cvresult2.iloc[:,0],c=\"green\",label=\"train,gamma=1\")\n","plt.plot(range(1,101),cvresult2.iloc[:,2],c=\"blue\",label=\"test,gamma=1\")\n","plt.legend()\n","plt.show()"],"execution_count":56,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABIUAAAEyCAYAAABku2DHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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pGM92ujJKF41MBUWiykqHJSoSmyAlUSMZ4WDgAAAAAAAMjFAKOlJOQ3fYn+Vo/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\nAADkIEIhAAAAAACAHPT/rQqUMq8sO4EAAAAASUVORK5CYII=\n","text/plain":["<Figure size 1440x360 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"metadata":{"id":"Q4_UkuDUHnG7","colab_type":"text"},"cell_type":"markdown","source":["# 4 XGBoost应用中的其他问题"]},{"metadata":{"id":"pj_mefcCHq8s","colab_type":"text"},"cell_type":"markdown","source":["## 1 过拟合：剪枝参数与回归模型调参"]},{"metadata":{"id":"Sz_yGy8trDpB","colab_type":"code","colab":{}},"cell_type":"code","source":["dfull = xgb.DMatrix(X,y)\n","\n","param1 = {'silent':True\n","          ,'obj':'reg:linear'\n","          ,\"subsample\":1\n","          ,\"max_depth\":6\n","          ,\"eta\":0.3\n","          ,\"gamma\":0\n","          ,\"lambda\":1\n","          ,\"alpha\":0\n","          ,\"colsample_bytree\":1\n","          ,\"colsample_bylevel\":1\n","          ,\"colsample_bynode\":1\n","          ,\"nfold\":5}\n","num_round = 200"],"execution_count":0,"outputs":[]},{"metadata":{"id":"BjiCHFDxrDpF","colab_type":"code","outputId":"5ab7b6b3-0670-444c-9afc-112c3f8910f4","colab":{"base_uri":"https://localhost:8080/","height":525},"executionInfo":{"status":"ok","timestamp":1550300582423,"user_tz":-480,"elapsed":1522,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["time0 = time()\n","cvresult1 = xgb.cv(param1, dfull, num_round)\n","print(datetime.datetime.fromtimestamp(time()-time0).strftime(\"%M:%S:%f\"))\n","\n","fig,ax = plt.subplots(1,figsize=(15,8))\n","ax.set_ylim(top=5)\n","ax.grid()\n","ax.plot(range(1,201),cvresult1.iloc[:,0],c=\"red\",label=\"train,original\")\n","ax.plot(range(1,201),cvresult1.iloc[:,2],c=\"orange\",label=\"test,original\")\n","ax.legend(fontsize=\"xx-large\")\n","plt.show()"],"execution_count":58,"outputs":[{"output_type":"stream","text":["00:00:321163\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAA2AAAAHWCAYAAAARnurlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nH10Tid6yw7adAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 1080x576 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"metadata":{"id":"8XBdH2RSrDpH","colab_type":"code","outputId":"4a416763-c6b9-4577-81ef-138d4146b915","colab":{"base_uri":"https://localhost:8080/","height":561},"executionInfo":{"status":"ok","timestamp":1550300598556,"user_tz":-480,"elapsed":2014,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["param1 = {'silent':True\n","          ,'obj':'reg:linear'\n","          ,\"subsample\":1\n","          ,\"max_depth\":6\n","          ,\"eta\":0.3\n","          ,\"gamma\":0\n","          ,\"lambda\":1\n","          ,\"alpha\":0\n","          ,\"colsample_bytree\":1\n","          ,\"colsample_bylevel\":1\n","          ,\"colsample_bynode\":1\n","          ,\"nfold\":5}\n","num_round = 200\n","\n","time0 = time()\n","cvresult1 = xgb.cv(param1, dfull, num_round)\n","print(datetime.datetime.fromtimestamp(time()-time0).strftime(\"%M:%S:%f\"))\n","\n","fig,ax = plt.subplots(1,figsize=(15,8))\n","ax.set_ylim(top=5)\n","ax.grid()\n","ax.plot(range(1,201),cvresult1.iloc[:,0],c=\"red\",label=\"train,original\")\n","ax.plot(range(1,201),cvresult1.iloc[:,2],c=\"orange\",label=\"test,original\")\n","\n","param2 = {'silent':True\n","          ,'obj':'reg:linear'\n","          ,\"max_depth\":2\n","          ,\"eta\":0.05\n","          ,\"gamma\":0\n","          ,\"lambda\":1\n","          ,\"alpha\":0\n","          ,\"colsample_bytree\":1\n","          ,\"colsample_bylevel\":0.4\n","          ,\"colsample_bynode\":1\n","          ,\"nfold\":5}\n","\n","param3 = {'silent':True\n","          ,'obj':'reg:linear'\n","          ,\"subsample\":1\n","          ,\"eta\":0.05\n","          ,\"gamma\":20\n","          ,\"lambda\":3.5\n","          ,\"alpha\":0.2\n","          ,\"max_depth\":4\n","          ,\"colsample_bytree\":0.4\n","          ,\"colsample_bylevel\":0.6\n","          ,\"colsample_bynode\":1\n","          ,\"nfold\":5}\n","\n","time0 = time()\n","cvresult2 = xgb.cv(param2, dfull, num_round)\n","print(datetime.datetime.fromtimestamp(time()-time0).strftime(\"%M:%S:%f\"))\n","\n","time0 = time()\n","cvresult3 = xgb.cv(param3, dfull, num_round)\n","print(datetime.datetime.fromtimestamp(time()-time0).strftime(\"%M:%S:%f\"))\n","\n","ax.plot(range(1,201),cvresult2.iloc[:,0],c=\"green\",label=\"train,last\")\n","ax.plot(range(1,201),cvresult2.iloc[:,2],c=\"blue\",label=\"test,last\")\n","ax.plot(range(1,201),cvresult3.iloc[:,0],c=\"gray\",label=\"train,this\")\n","ax.plot(range(1,201),cvresult3.iloc[:,2],c=\"pink\",label=\"test,this\")\n","ax.legend(fontsize=\"xx-large\")\n","plt.show()"],"execution_count":59,"outputs":[{"output_type":"stream","text":["00:00:334137\n","00:00:144793\n","00:00:184795\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAA2AAAAHWCAYAAAARnurlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzs3Xd8XOWV+P/PLdNHU1Rd5So3XCgu\nGBsbbENoofcWAqnkm4Qkm7LZXxq7m+ymbRLSGyEJ3YDBFAMGO6YaMMUG96piq47q9Ft+f4wkS7aa\n5ZHkct56zWvGuneeeyxr5Dk6z3MexbZtGyGEEEIIIYQQA04d6gCEEEIIIYQQ4mQhCZgQQgghhBBC\nDBJJwIQQQgghhBBikEgCJoQQQgghhBCDRBIwIYQQQgghhBgkkoAJIYQQQgghxCDRezth/fr1fPnL\nX6akpASASZMm8Z3vfGfAAxNCCCGEEEKIE02vCRjA3Llz+dWvfjXQsQghhBBCCCHECU2mIAohhBBC\nCCHEIOlTArZz504+97nPccMNN/Daa68NdExCCCGEEEIIcUJSbNu2ezqhqqqKDRs2cOGFF1JWVsat\nt97KCy+8gNPp7PL8mprmAQl0qATeuwZX7fPUnluBrecAkDt3FiQSRDZuG+LoxGBLJGDKFD9FRTZv\nvhlFUbo+z1tdi6+2jqaRw0kGA72O25xq4uwH51Edr2L1Na8wLe+UrMT79ttv8PbbbzB9+iwWLVqa\nlTGFEEIIIUTPCgpyuj3WawWsqKiIiy66CEVRKC4uJj8/n6qqqqwGeExTPZl7K9H+KSu/ALW2Bixr\niIISQ8XthqVLDfbsUdm6tfuXT1vS5W5o6tO4Oc4AP1n8fxiWwVfX/D9My8xKvKefPodwOJcPP/yA\nAwf2Z2VMIYQQQgjRf70mYE899RR/+ctfAKipqaGuro6ioqIBD+xYYWtuABQz3v45q6AQxTBQGuqH\nKiwxhC6+2ADg2We772Fjupyk3W4c0SiKYfRp3PPGXsCVJdfwbvUG/rjxd1mJVdN0zjnnPADWrn0B\n0+xbLEIIIYQQYmD0moAtWbKEt99+mxtvvJE777yT73//+91OPzwR2aoXODwBA1Bra4ckJjG0li0z\ncDhsVq7suYloIhRAAdyNfauCAfzXwv8lz53Hj9bfzfZIdqa4Dh8+klNOmUV9fYR33307K2MKIYQQ\nQoj+6TUB8/v9/P73v+eBBx7g0UcfZfHixYMR1zGjvQJmdUjA8vMBUGuqhyQmMbQCAViyxGTzZo0t\nW3qYhhjIwQZcR5CA5Xvy+cniX5IwE9z50qdJmaksRAxnnrkQn8/Hhg1vEYnUZWVMIYQQQghx5KQN\nfS9sLVMBo6sKmCRgJ61rr00D8Oij3VfBbF0n5ffhSCTRksk+j33JhEu5fspNbKx5n5+98z9HHSuA\ny+Vi0aKlWJbJ2rUv0kvvHSGEEEIIMUAkAeuN2kUFrFASsJPdeecZBAI2y5c7MHvol3GkzTja/PfC\n/6U4Zwy/fPfnvHVg/dGE2m7cuImMHz+Rysr9bN68KStjCiGEEEKIIyMJWC/aKmAd14DZrRUwpbZm\nSGISQ8/thssuS1NZqfLqq1q35yVz/FiqiqupCY6g6pTjDPDrpX/Atm2+8NKnaUllZ3uHhQvPxeFw\n8sYbrxCLRbMyphBCCCGE6DtJwHphd1UByy8AQK2RBOxkds01mY6Cjzzi6P4kVSUZ8KOlDRyxePfn\ndeHMEWfxxdO+wr6mvdz9xnePJtR2fn8OZ565kFQqyauvrs3KmEIIIYQQou8kAeuFrWX2AevcBbEt\nAZMpiCezefNMiostnnlGp6Wl+/PapiEeSTOONt+Y+20mhSdz30d/ZVPNB/0NtZNTTplJUdEwdu7c\nxr59e7IyphBCCCGE6BtJwHrTvhFzhymIgSC20ykJ2ElOUeCaa9LEYkqPe4KlvV5MXcfV1HzEm3c7\nNSf/tfB/sbH59qvfyErzDFVVWbz4PFRVZd26l0in00c9phBCCCGE6BtJwHrRVQUMRcHKL5B9wATX\nXNPWDbGHaYiKQjIYQLUsnC1Hvu7qnNFLuHDcJaw/8AZP7Fze31A7yc8vYNasM2hubuLVV9dIV0Qh\nhBBCiEEiCVgvbLWLBIxMK3q1pvqIGiuIE8/48TazZ5u88orGgQNKt+cl+tkNsc3dC36IS3Pxg9e/\nQ0u6h/mOR2D27DPJzy9gy5YPee892aBZCCGEEGIwSALWi/YKmHVoAlaAEo+jRLPzZlgcv669No1l\nKTz2WPfTEE23C8PlwtnSgmIYR3yNMYGxfOG0L3Mgup9fbfj50YTbzuFwcPHFV+D35/Dmm6+yffuW\nrIwrhBBCCCG6JwlYL9oSMLqogAEo0gnxpHfZZWmcTpsHH3T0WBBNhAIokFkL1g9fOu2rjPSP4rfv\n/4rdjbv6F+whfD4/l1xyBU6ni5dffoGKirKsjCuEEEIIIbomCVhv1K4rYLa0ohetwmG45BKDHTs0\n1q/vYU+wQAAbcPejGyKA1+Hl+2f9FykrxZdfvhPDOvJKWldyc/O54IKPAzarVj1FJCJrG4UQQggh\nBookYL3osgkH0opedHbrrZlmHH//e/fNOCyHTtrnxRFPoKZS/brOpROu4NIJV7D+wBv86t3sTEUE\nGDWqmHPPPZ9kMsnKlY/T3Ny/JFEIIYQQQvRMErBe2N1UwNqmIEoCJgDmzzeZMMFi5Uqd+vruz2tv\nxtHPKpiiKPx08S8Y4RvJT97+ERuqstc8Y/LkaZx11mKi0Raeemo5sVgsa2MLIYQQQogMScB60X0F\nrDUBq5UpiCKzJ9gtt6RIJpUeW9InAznYipLphtjPDpohd5jfLPsjlm3xuRfvoCXVvzVlXTn11DM4\n/fS5NDY28PTTj5NKJbM2thBCCCGEkASsd6o7c39oApYvUxBFZ9ddZ+B02vzjHz0041BVkjl+tHQa\nPR7v5qTeLRh5Nl887Svsa9rLt1/9Rr/H6cq8eQuYNm0GtbXVPPvsk7JRsxBCCCFEFkkC1htFxVbd\nPUxBlAqYyMjLs7n4YoNt2zTeeqv7ZhyJcBAAT33jUV3vG3O/zayC03ho6/08v/e5oxqrI0VRWLRo\nKRMmlLB/fzkrVz5GItH/ZFEIIYQQQhwkCVgf2Kr7sCmIdm4utqpKBUx0csstmWrRP/7R/TTEtNeL\n4XTgampGMc1+X8upOfn10j+gqzr/36vfJGEk+j3WoVRVZdmyiygpmUJl5X5WrHiElpbsTXUUQggh\nhDhZSQLWB7bmPawChqZh5+ahyBow0cGCBSbjxlk89ZROQ0M3JykKiVAQxbZx9bMZR5vJuVP41IzP\nsa9pL799/1dHNdahNE1j2bILmTHjNCKROp544mEaGnroMCKEEEIIIXolCVgf2Kr7sDVgkJmGKFMQ\nRUdtzTgSCYXly7uvgiVCQWzAU9/Q72Ycbb4+51sUeAr55bs/o7w5uxspK4rCwoXnMHfuWTQ3N/HE\nEw8TidRl9RpCCCGEECcTScD6QvOiWIdP77IKClGbGiGRvalf4vh33XUGmmbz0EPdJ2C2rpMM5KAn\nU+jxo/v+yXEG+O78u4kbcb73+n8c1VhdURSF2bPPZNGiJcTjMZ56ajmNjVIJE0IIIYToD0nA+sDW\n3Cjm4XsitW/GXFc72CGJY1hBgc3SpSYbN2ps2dL9SywRyjTjcHc7V7Hvrpl8PbOL5rJy1wrWla89\n6vG6Mn36qSxYcA6xWJQnn1wumzULIYQQQvSDJGB9YKteFDsNdueGCdKKXnTnuusyzTgeeaSHZhw+\nL6bDgbvx6JpxAKiKyv8s+ikKCt9+5etE09GjGq87s2adzplnLqSlpZknn3xUGnMIIYQQQhwhScD6\nwNa62QusvRW9JGCis/POMwgGbZYv1+k2t1IU4uHsNOMAmFlwKrfP+DTb67dx67PXEzcGpnX86afP\nZfbsM2lqauSpp5YTiw1MsieEEEIIcSKSBKwvVC/A4XuBFcpeYKJrbjdcfnmaqiqVdet62BOsvRlH\n41E34wC4+6wfceG4S3il4l/cvupmkmbyqMfsypw58znttNk0NNTz1FPLiR/FptJCCCGEECcTScD6\noK0CdtheYK1rwKQVvejKtddmpiE+/HDPzThSOX70ZBI9C81cHJqDP55/L0uLz+Ol0hf59Au3kTbT\nRz3uoRRF4cwzz25vUb9y5WMkk9KMRgghhBCiN5KA9YHdVgE7dAqirAETPZg922L8eIvnntNp7mGp\nVDwcAsBd35iV67o0F3+94J+cPeocVu15hjtXfxrLtrIydkdtLeqnTZtBbW01Tz/9OKlUKuvXEUII\nIYQ4kUgC1gftFbBDpyDKGjDRA0XJVMHicYWVK/Vuz8s049BxNzYddTOONh7dw98vfJB5w+fz5K7H\n+e83f5CVcQ+lKAqLFy9j0qSpVFVVSiVMCCGEEKIXkoD1ga1lKmCHNeEoLMJ2OtH27hmCqMTx4Oqr\ne++GiKKQCIUyzTiastdV0Ofwcd+FDzA+OIF73vs/7t/896yN3ZGiKCxZ8jFKSqZQVXWAJ59cTjx+\n+LYNQgghhBBCErC+UbuugKHrmBNK0LZty0oDBXHiKS62WbDA4PXXdfbtU7o9LxEKYAPu+qPfE6yj\nXHceD1z8KGFXmK+vu4tXyv+V1fHbqKrK0qUXtE9HXLHiEaLRlgG5lhBCCCHE8UwSsD5oq4AdugYM\nwJgyBTXaglpeNthhieNE255g99/ffRXMcjhI5fhxJJLo8exO4RsfmsjfLnwABYXbn7+FHfXbszp+\nG1VVWbx4GbNmnUF9fYQnnniYpqbsrGsTQgghhDhRSALWB3Z3FTDAnDQFAH3blkGNSRw/LrvMIBy2\n+ec/HSR76AqfCAUBcDdktwoGMH/EAn5+zj00Jhu4bMWFrD/wZtavAZnpiGedtajTPmGyWbMQQggh\nxEGSgPWBrXmAbipgk6cCoG3dOqgxieOHxwM33JCmtlblqae6b8aR8vswdT2zKbOV/a6F1025kR+d\n/VPqExGufPJi/rH5b1m/BmSSsLlzz2LOnPmyWbMQQgghxCEkAesLNZOA0VUFbEomAdO3SwImunfb\nbSkUxeavf3V2f5KikAgHUS0bd2PTgMRxx4zP8MjHV+B3+Pna2i/xrXVfG5B9wgBmzz6TU09t26z5\nMRIJ2axZCCGEEEISsD7oqQJmjh2X6YQoUxBFD8aOtVm2zGTDBo0PPuj+ZZcIBQ824xigxi5nj1rM\n81evZWruKfz1wz9xy3PXETeynxwpisL8+WczffosIpFaVq58XFrUCyGEEOKkJwlYH9hq9wlYWydE\nfdu2AZk2Jk4ct9+e2aS4pyrYQDbj6GhscBzPXPUiS4vP4+XS1dz0zDW0pLPftVBRFM4+ewlTppxC\nTU2VtKgXQgghxElPErA+aK+AdTEFETKdEJVYVDohih6de67J2LEWTzyhE4l0f148NwyAJ1I/oPH4\nHX7uu/BBLh5/Ka9WrOPapy6nMZn9BiCKonDOOecxdep0aVEvhBBCiJOeJGB90JaAHboRcxtzsqwD\nE71TVfjkJ1MkEgoPPth9S/q014PhcuJqakZND8z6rDZOzcmfzv8bV5VcyztVb3HVU5cSSdRl/Tqq\nqnLOOecxc+bp0qJeCCGEECc1ScD6Qu2lAtbail46IYre3HBDGo/H5t57nZhmNycpCvHcMArZ35i5\nK7qq8+ulf+CWabexseZ9PvHcjSSM7E9/VBSFBQsWM3v2PJqaGnniiYepq6vJ+nWEEEIIIY5lkoD1\nQU9NOKBDJ0RpxCF6EQrB1VenKS1VWbWq+5b0iWAAS1Px1DcOytpCTdX4yeJfcPnEK1l/4A2+9PLn\nsOzsXzfTon4B8+cvIhpt4fHHH2bfvj1Zv44QQgghxLFKErA+sHupgEknRHEkPvvZzLTC3/ymh5b0\nqkoiFEI1TVxNg7ORsaqo/GrJ75k3fD4rdj7OD9+8e8Cuddppszn//IuxLJNnn13Bpk3vDdi1hBBC\nCCGOJZKA9UFvFTB0HXPiJPTt26UToujVpEkWH/uYwTvvaKxfr3V7Xjw3hE1rM44Bakl/KLfu5r4L\nH2B8cAK/eu/n/P2jewfsWhMnTubyy6/F7fbwyitrWLv2RRIJaVMvhBBCiBObJGB9oboz990lYIAx\nebJ0QhR99oUvZFrS/+Y33Tfj6NiS3hEbvE2Mc915PHjJY+S58/jmuq+ycteTA3atoqLhXHXVDeTm\n5rF58ybuv/8vvPfe2xjGwDYfEUIIIYQYKpKA9YWiYqvubqcgQodOiDINUfTBvHkmZ5xhsmqVgx07\nun8ZtrWkH4xmHB2NC47nnxc/glv38NkXP8lze54ZsGsFAkGuvvomzjprEQBvvPEK999/L9u3y2tJ\nCCGEECceScD6yFbd3U9BBIzWBEw6IYq+UJSDVbDf/a5vLekVwxis8AA4o2gOD17yGE7Vxaeev5UX\n964asGvpus6pp87m5pvv4LTT5pBIxFm9+jlefvl50gPcil8IIYQQYjBJAtZHtubtpQKWaUUvFTDR\nVxdeaDB+vMUjjzioqlK6PklRiIdDmZb0DYO/b9aZw+fzwMWPoqs6n1x1My+Xrh7Q67lcbubPP5vr\nr/8EBQWFbN36EY899gCRSPb3JhNCCCGEGAqSgPWRrbp7XAPW3glRNmMWfaRp8PnPp0ilFP785+6r\nYMlgAFtRMi3pB6kZR0dnjVzIPy56GFVR+cRzN/Ds7qcH/JrBYIgrr7yeGTNOJRKpY/ny+9m2bfOA\nX1cIIYQQYqBJAtZXmhfF6qFDW3snxG3SCVH02bXXpsnPt7j3XieN3RS4bE0jGchBS6dxRGODG2Cr\nRaPO4R8XPYym6Nz+/M08sOUfA35NTdM5++wlnH/+JSiKyksvrZIpiUIIIYQ47kkC1ke25kYxe37z\na0yZghKLoZaVDlJU4njn8cDnP5+mqUnhD3/ofl+weDiUOX+Qm3F0tHj0uTx22VMEnUHuWvMF7nnv\nF4Ny3YkTJ3HttTeRn39wSmJ9fWRQri2EEEIIkW2SgPWRrXpR7DRY3TdCMCfJOjBx5G6/PUV+vsUf\n/uCkvr7rcwyPG8PlwtncMujNODo6o2gOK694gRG+kfznG9/lv9/8waBcNxgMc+WV1zN9+iwikToe\nffR+PvzwfSypNgshhBDiOCMJWB/ZWmYvsJ4acRzshCgJmOg7nw+++MUUzc0Kv/99N1UwRSEeDqJA\nZi3YEJqUO5mnr3yBCaGJ/PLdn3Hvh38elOvqus6iRUs5//yLUVWVdete5vHHH6KmpnpQri+EEEII\nkQ2SgPWV6s3c97AOzJg5CwDHuxsGIyJxAvnEJ9IUFlr88Y9O6uq67ojY1ozD3TA0zTg6GpUzmocu\neZx8Tz7ffuXrrCl9adCuPXHiZG688TZKSqZQXV3J8uX38+qra0ilkoMWgxBCCCFEf0kC1kftFbAe\nOiFao0ZjjhiJ4603hvwNsji+eL3wpS+liEYVfvvbrjsi2ppGIhgY0mYcHY0JjOVvFzyIrup86oVP\nsC0yeB1AvV4f5513ER//+FUEAkE2bnyPBx/8G7t2bceW154QQgghjmGSgPWR3VoB6ykBQ1FIz52H\nWluLtmfXIEUmThS33JJm2DCLv/zFSU1N11WwRDgIDG0zjo7mDp/HL879Dc2pJm569lpq47WDev3R\no8dw3XW3MmfOfOLxBM8//zTPPPMEjY3HxtdHCCGEEOJQ2ve///3vZ3PAWCyVzeGOGY7IWhyNb5MY\neQuWa1i356lVlbheepH0zFMxp88cxAjF8c7hAKcTVq1yYNtw7rnmYedYuo6zpQVHLE4iFMLWhv53\nKNPyTsG2bZ7b8zSP73iUkCvEtLxTUJXBiU1VVUaOHM3EiZOpr49QVraPzZs3YhgGhYXD0DRtUOIQ\nQgghhGjj87m6PTb0796OE7bWugaspwoYYMw9EwDHW28OdEjiBHTzzWlGjLC4914HVVVdVMEUhUQ4\nhAKZtWDHiK/P+Xf+bfa3qE9E+PKaO1nyyAJW73t+UKcDhkJhPv7xqzjvvItwuz28++5b3H//X9m8\neZN0SxRCCCHEMUMSsL5Se++CCGBMm47t9UkCJvrF5YKvfCVFIqFwzz1dd0RMBnJam3E0HDNrDRVF\n4Rtzv82bN73HDVNuZmtkCzc+cw03PHMVexv3DGocJSVTuPHGTzJnznzS6RRr177II4/8k717d8n6\nMCGEEEIMOUnA+qitAtbjGjAAXSc9ey769m0okbpBiEycaG64Ic3o0Rb33efgwIHDq2AHm3EYOFui\nQxBh90b4R/LLJb9lzXWvs2jUubxcuppFD83j/975CUlz8LoUOhwO5syZz403fpLJk6cRidTy7LNP\n8vjjD1FRUTZocQghhBBCHErWgPWR3rwRV+3zpAovxvRP6/Fcbe8enK+/SnreWZgTSwYpQnGi0DTw\n++GZZxykUrBsWddrwTwNjSiWTTIYGIIoe1bgLeSaSddTEp7EaxWv8Py+53h615Pke/IpCU0atPVh\nTqeL8eMnMmFCCbFYlPLyUrZt20xVVSVFRcNxu92DEocQQgghTi6yBiwLbM0D9KECBqRlHZg4Stde\nm2bMGIt//tNBefnhVTDD4ybtduFsaUFNp4cgwt4pisIVJVfz+o3vcPv0T7OrcSeffuE2Fjw4m39u\nvm9QK2K5uflccMGlXHXVjYwcOZrS0j089NB9vPXW6xiGMWhxCCGEEEJIBayP9OhOXNUrSOUtwQie\n0eO5dn4+nnt+gQIkbrxlcAIUJxRNg0DA5umnHcTjcP75h1fBAFwtUWxNJe3zDnKEfefW3Swb8zGu\nLLmapJnkjf2v8eyep3lo6/14dA/T8qajqYPTqdDv9zN58jTC4TwOHChn37497NixFa/XTzici6J0\n3f5fCCGEEOJI9FQBU+wsr0qvqWnO5nDHDGfNcwTfv46Wkv8kPvbLvZ4fWrIQfcc2aneWZzorCHGE\nDAMWLvRRWqrw5ptRiosPealaFnnbd2GrKpGS8XCcJA8HWvbzuw9+zX0f/YW4Eac4MJavz/4WV0+6\nbtASMYBUKsXbb7/Bxo3vYts24XAeZ5wxl4kTJ6OqMjlACCGEEP1XUJDT7TGpgPWRmqjAfeBB0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55iUUy8J2u0nPmZe5zT0TY85cbFlYLw5RXa0wf74PTYM332wht8P2X3osRnhvGSmfl8YxUgUb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\n","text/plain":["<Figure size 1080x576 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"metadata":{"id":"1BDR8JuTHzvH","colab_type":"text"},"cell_type":"markdown","source":["## 2 XGBoost模型的保存和调用"]},{"metadata":{"id":"C932reJbH3G0","colab_type":"text"},"cell_type":"markdown","source":["### 使用Pickle保存和调用模型"]},{"metadata":{"id":"scby1FwzrDpM","colab_type":"code","colab":{}},"cell_type":"code","source":["import pickle"],"execution_count":0,"outputs":[]},{"metadata":{"id":"34zvBXoPrDpR","colab_type":"code","colab":{}},"cell_type":"code","source":["dtrain = xgb.DMatrix(Xtrain,Ytrain)\n","\n","#设定参数，对模型进行训练\n","param = {'silent':True\n","          ,'obj':'reg:linear'\n","          ,\"subsample\":1\n","          ,\"eta\":0.05\n","          ,\"gamma\":20\n","          ,\"lambda\":3.5\n","          ,\"alpha\":0.2\n","          ,\"max_depth\":4\n","          ,\"colsample_bytree\":0.4\n","          ,\"colsample_bylevel\":0.6\n","          ,\"colsample_bynode\":1}\n","num_round = 180\n","\n","bst = xgb.train(param, dtrain, num_round)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"QYnCbDr9rDpT","colab_type":"code","colab":{}},"cell_type":"code","source":["#保存模型\n","pickle.dump(bst, open(\"xgboostonboston.dat\",\"wb\"))\n","\n","#注意，open中我们往往使用w或者r作为读取的模式，但其实w与r只能用于文本文件 - txt\n","#当我们希望导入的不是文本文件，而是模型本身的时候，我们使用\"wb\"和\"rb\"作为读取的模式\n","#其中wb表示以二进制写入，rb表示以二进制读入，使用open进行保存的这个文件中是一个可以进行读取或者调用的模型"],"execution_count":0,"outputs":[]},{"metadata":{"id":"Wa5RMT8arDpU","colab_type":"code","outputId":"aa793454-6e95-4980-f49a-006609cd7dd6","colab":{"base_uri":"https://localhost:8080/","height":179},"executionInfo":{"status":"ok","timestamp":1550300736137,"user_tz":-480,"elapsed":859,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["#看看模型被保存到了哪里？\n","import sys\n","sys.path"],"execution_count":63,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['',\n"," '/env/python',\n"," '/usr/lib/python36.zip',\n"," '/usr/lib/python3.6',\n"," '/usr/lib/python3.6/lib-dynload',\n"," '/usr/local/lib/python3.6/dist-packages',\n"," '/usr/lib/python3/dist-packages',\n"," '/usr/local/lib/python3.6/dist-packages/IPython/extensions',\n"," '/root/.ipython']"]},"metadata":{"tags":[]},"execution_count":63}]},{"metadata":{"id":"7FO6vkW0rDpV","colab_type":"code","colab":{}},"cell_type":"code","source":["#重新打开jupyter lab\n","\n","from sklearn.datasets import load_boston\n","from sklearn.model_selection import train_test_split as TTS\n","from sklearn.metrics import mean_squared_error as MSE\n","import pickle\n","import xgboost as xgb\n","\n","data = load_boston()\n","\n","X = data.data\n","y = data.target\n","\n","Xtrain,Xtest,Ytrain,Ytest = TTS(X,y,test_size=0.3,random_state=420)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"AxB8B6a_rDpX","colab_type":"code","colab":{}},"cell_type":"code","source":["#注意，如果我们保存的模型是xgboost库中建立的模型，则导入的数据类型也必须是xgboost库中的数据类型\n","dtest = xgb.DMatrix(Xtest,Ytest)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"q_xQtZKrrDpY","colab_type":"code","outputId":"5a37f37d-9668-4746-9b2f-27cc022734fb","colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"status":"ok","timestamp":1550300770449,"user_tz":-480,"elapsed":820,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["#导入模型\n","loaded_model = pickle.load(open(\"xgboostonboston.dat\", \"rb\"))\n","print(\"Loaded model from: xgboostonboston.dat\")"],"execution_count":66,"outputs":[{"output_type":"stream","text":["Loaded model from: xgboostonboston.dat\n"],"name":"stdout"}]},{"metadata":{"id":"ggwlx0aHrDpa","colab_type":"code","colab":{}},"cell_type":"code","source":["#做预测，直接调用接口predict\n","ypreds = loaded_model.predict(dtest)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"QjAQdpYMrDpb","colab_type":"code","outputId":"e3ef2f84-1a2e-4af7-d08d-ac9de465c591","colab":{"base_uri":"https://localhost:8080/","height":575},"executionInfo":{"status":"ok","timestamp":1550300778025,"user_tz":-480,"elapsed":802,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["ypreds"],"execution_count":68,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([ 8.796627 , 22.251627 , 29.463833 , 13.552206 ,  9.749415 ,\n","       21.718597 , 15.428588 , 15.765638 , 16.23822  , 15.386694 ,\n","       21.821184 , 35.972435 , 20.940928 , 30.260195 , 21.311058 ,\n","       11.721132 , 21.283613 , 27.035889 , 25.570278 , 23.767391 ,\n","       17.878729 , 16.17358  , 25.787714 , 22.070162 , 19.809326 ,\n","       16.368736 , 22.975214 , 23.519804 , 23.280918 , 16.69544  ,\n","       35.166664 , 20.457819 , 20.176447 , 23.672691 , 23.454966 ,\n","       25.159687 , 16.155891 , 24.84578  , 18.094835 , 34.196056 ,\n","       18.00448  , 20.653    , 35.82867  , 19.253775 , 15.19614  ,\n","       27.488487 , 42.640575 , 14.962704 , 10.649249 , 37.515667 ,\n","       25.812563 , 20.99281  , 20.339155 , 46.622208 , 27.54994  ,\n","       25.598074 , 18.92313  , 20.27878  , 18.078022 , 18.10919  ,\n","       15.38769  , 24.090042 , 18.959557 , 32.263603 , 28.549206 ,\n","       19.746824 , 21.575943 , 17.493204 , 22.469473 , 16.78368  ,\n","       28.558378 , 39.192738 , 30.741638 , 23.411602 , 20.260756 ,\n","       23.6142   , 39.259674 , 26.241362 , 21.332632 , 20.409172 ,\n","       18.60242  , 45.13576  , 22.842415 ,  9.231186 , 25.762651 ,\n","       22.870018 , 17.553167 , 21.203955 , 18.381695 , 12.339012 ,\n","       20.973583 , 19.36628  , 40.08989  , 32.313454 , 23.379137 ,\n","       11.256797 , 15.843573 , 21.447554 ,  9.139374 , 11.592586 ,\n","       32.25905  , 15.888885 , 24.635048 , 24.568998 , 34.33937  ,\n","       42.184566 , 20.542372 ,  9.037175 , 23.411602 , 15.452425 ,\n","       44.07828  , 21.949333 , 22.537151 , 24.824223 , 19.482115 ,\n","       28.880598 , 24.445131 , 18.60572  , 42.940426 , 17.63903  ,\n","       23.849207 , 25.294277 , 16.641256 , 18.348835 , 15.79845  ,\n","       23.484095 , 31.655083 , 11.04593  , 21.427174 , 18.99448  ,\n","       14.666879 , 20.135597 ,  8.577621 , 28.822554 , 29.861868 ,\n","       21.066494 , 19.01611  , 20.400019 , 10.22956  , 17.627537 ,\n","       40.506153 , 17.923716 , 23.602158 , 19.466068 , 31.714619 ,\n","       19.53678  , 11.9984665, 37.518803 , 25.43734  , 22.104725 ,\n","       16.810064 , 26.180004 ], dtype=float32)"]},"metadata":{"tags":[]},"execution_count":68}]},{"metadata":{"id":"msrpLgDlrDpd","colab_type":"code","outputId":"a62b4198-185c-4d9c-ba3f-c50fe2aa7114","colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"status":"ok","timestamp":1550300789814,"user_tz":-480,"elapsed":813,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["from sklearn.metrics import mean_squared_error as MSE, r2_score\n","MSE(Ytest,ypreds)"],"execution_count":69,"outputs":[{"output_type":"execute_result","data":{"text/plain":["9.687680910474723"]},"metadata":{"tags":[]},"execution_count":69}]},{"metadata":{"id":"_oZuWm0ErDpg","colab_type":"code","outputId":"3c72d999-9b89-43d7-9bbe-3805211ebdef","colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"status":"ok","timestamp":1550300792607,"user_tz":-480,"elapsed":830,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["r2_score(Ytest,ypreds)"],"execution_count":70,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.8958917093450866"]},"metadata":{"tags":[]},"execution_count":70}]},{"metadata":{"id":"t-SB3FH-IQtt","colab_type":"text"},"cell_type":"markdown","source":["### 使用Joblib保存和调用模型"]},{"metadata":{"id":"APf4GPnMrDpi","colab_type":"code","colab":{}},"cell_type":"code","source":["bst = xgb.train(param, dtrain, num_round)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"nhXrHwddrDpk","colab_type":"code","outputId":"47f08c29-c015-4bc3-ec5c-93439754237c","colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"status":"ok","timestamp":1550300824820,"user_tz":-480,"elapsed":824,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["import joblib\n","\n","#同样可以看看模型被保存到了哪里\n","joblib.dump(bst,\"xgboost-boston.dat\")"],"execution_count":72,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['xgboost-boston.dat']"]},"metadata":{"tags":[]},"execution_count":72}]},{"metadata":{"id":"tEu-m8h5rDpm","colab_type":"code","colab":{}},"cell_type":"code","source":["loaded_model = joblib.load(\"xgboost-boston.dat\")"],"execution_count":0,"outputs":[]},{"metadata":{"id":"r6g0gdGxrDpp","colab_type":"code","colab":{}},"cell_type":"code","source":["dtest = xgb.DMatrix(Xtest,Ytest)\n","ypreds = loaded_model.predict(dtest)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"SAMOzFp6rDpr","colab_type":"code","outputId":"bf9669b3-804a-4217-a7e4-8ba41f9adcfa","colab":{"base_uri":"https://localhost:8080/","height":575},"executionInfo":{"status":"ok","timestamp":1550300836331,"user_tz":-480,"elapsed":797,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["ypreds"],"execution_count":75,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([ 8.796627 , 22.251627 , 29.463833 , 13.552206 ,  9.749415 ,\n","       21.718597 , 15.428588 , 15.765638 , 16.23822  , 15.386694 ,\n","       21.821184 , 35.972435 , 20.940928 , 30.260195 , 21.311058 ,\n","       11.721132 , 21.283613 , 27.035889 , 25.570278 , 23.767391 ,\n","       17.878729 , 16.17358  , 25.787714 , 22.070162 , 19.809326 ,\n","       16.368736 , 22.975214 , 23.519804 , 23.280918 , 16.69544  ,\n","       35.166664 , 20.457819 , 20.176447 , 23.672691 , 23.454966 ,\n","       25.159687 , 16.155891 , 24.84578  , 18.094835 , 34.196056 ,\n","       18.00448  , 20.653    , 35.82867  , 19.253775 , 15.19614  ,\n","       27.488487 , 42.640575 , 14.962704 , 10.649249 , 37.515667 ,\n","       25.812563 , 20.99281  , 20.339155 , 46.622208 , 27.54994  ,\n","       25.598074 , 18.92313  , 20.27878  , 18.078022 , 18.10919  ,\n","       15.38769  , 24.090042 , 18.959557 , 32.263603 , 28.549206 ,\n","       19.746824 , 21.575943 , 17.493204 , 22.469473 , 16.78368  ,\n","       28.558378 , 39.192738 , 30.741638 , 23.411602 , 20.260756 ,\n","       23.6142   , 39.259674 , 26.241362 , 21.332632 , 20.409172 ,\n","       18.60242  , 45.13576  , 22.842415 ,  9.231186 , 25.762651 ,\n","       22.870018 , 17.553167 , 21.203955 , 18.381695 , 12.339012 ,\n","       20.973583 , 19.36628  , 40.08989  , 32.313454 , 23.379137 ,\n","       11.256797 , 15.843573 , 21.447554 ,  9.139374 , 11.592586 ,\n","       32.25905  , 15.888885 , 24.635048 , 24.568998 , 34.33937  ,\n","       42.184566 , 20.542372 ,  9.037175 , 23.411602 , 15.452425 ,\n","       44.07828  , 21.949333 , 22.537151 , 24.824223 , 19.482115 ,\n","       28.880598 , 24.445131 , 18.60572  , 42.940426 , 17.63903  ,\n","       23.849207 , 25.294277 , 16.641256 , 18.348835 , 15.79845  ,\n","       23.484095 , 31.655083 , 11.04593  , 21.427174 , 18.99448  ,\n","       14.666879 , 20.135597 ,  8.577621 , 28.822554 , 29.861868 ,\n","       21.066494 , 19.01611  , 20.400019 , 10.22956  , 17.627537 ,\n","       40.506153 , 17.923716 , 23.602158 , 19.466068 , 31.714619 ,\n","       19.53678  , 11.9984665, 37.518803 , 25.43734  , 22.104725 ,\n","       16.810064 , 26.180004 ], dtype=float32)"]},"metadata":{"tags":[]},"execution_count":75}]},{"metadata":{"id":"vU52FLP0rDpt","colab_type":"code","outputId":"4bdb9cb7-da2c-4265-bfa1-8650886984cf","colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"status":"ok","timestamp":1550300838518,"user_tz":-480,"elapsed":802,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["MSE(Ytest, ypreds)"],"execution_count":76,"outputs":[{"output_type":"execute_result","data":{"text/plain":["9.687680910474723"]},"metadata":{"tags":[]},"execution_count":76}]},{"metadata":{"id":"_-oj1h-9rDpv","colab_type":"code","outputId":"f205bf40-b411-4bf3-d757-5e813a147d8c","colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"status":"ok","timestamp":1550300848553,"user_tz":-480,"elapsed":820,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["r2_score(Ytest,ypreds)"],"execution_count":77,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.8958917093450866"]},"metadata":{"tags":[]},"execution_count":77}]},{"metadata":{"id":"bYBEl3WArDpx","colab_type":"code","colab":{}},"cell_type":"code","source":["#使用sklearn中的模型\n","from xgboost import XGBRegressor as XGBR\n","\n","bst = XGBR(n_estimators=200\n","           ,eta=0.05,gamma=20\n","           ,reg_lambda=3.5\n","           ,reg_alpha=0.2\n","           ,max_depth=4\n","           ,colsample_bytree=0.4\n","           ,colsample_bylevel=0.6).fit(Xtrain,Ytrain) #训练完毕"],"execution_count":0,"outputs":[]},{"metadata":{"id":"OczHC9tMrDpz","colab_type":"code","outputId":"237b7925-b7da-49d6-de62-ee959292d04c","colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"status":"ok","timestamp":1550300865542,"user_tz":-480,"elapsed":783,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["joblib.dump(bst,\"xgboost-boston-sklearn.dat\")"],"execution_count":79,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['xgboost-boston-sklearn.dat']"]},"metadata":{"tags":[]},"execution_count":79}]},{"metadata":{"id":"6G0YVH83rDp0","colab_type":"code","colab":{}},"cell_type":"code","source":["loaded_model = joblib.load(\"xgboost-boston-sklearn.dat\")"],"execution_count":0,"outputs":[]},{"metadata":{"id":"6wPkHUlbrDp0","colab_type":"code","colab":{}},"cell_type":"code","source":["#则这里可以直接导入Xtest,直接是我们的numpy\n","ypreds = loaded_model.predict(Xtest)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"jiT7RuV_rDp2","colab_type":"code","outputId":"c9a7bf5b-9827-4a80-96ab-23c09d591315","colab":{}},"cell_type":"code","source":["Xtest"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[4.15292e+01, 0.00000e+00, 1.81000e+01, ..., 2.02000e+01,\n","        3.29460e+02, 2.73800e+01],\n","       [2.73100e-02, 0.00000e+00, 7.07000e+00, ..., 1.78000e+01,\n","        3.96900e+02, 9.14000e+00],\n","       [3.15000e-02, 9.50000e+01, 1.47000e+00, ..., 1.70000e+01,\n","        3.96900e+02, 4.56000e+00],\n","       ...,\n","       [5.08300e-02, 0.00000e+00, 5.19000e+00, ..., 2.02000e+01,\n","        3.89710e+02, 5.68000e+00],\n","       [3.77498e+00, 0.00000e+00, 1.81000e+01, ..., 2.02000e+01,\n","        2.20100e+01, 1.71500e+01],\n","       [1.96091e+01, 0.00000e+00, 1.81000e+01, ..., 2.02000e+01,\n","        3.96900e+02, 1.34400e+01]])"]},"metadata":{"tags":[]},"execution_count":44}]},{"metadata":{"id":"XijbSBvfrDp7","colab_type":"code","outputId":"db8d400e-b765-4417-a4f8-7eec6449760c","colab":{}},"cell_type":"code","source":["dtest"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<xgboost.core.DMatrix at 0x29e30670668>"]},"metadata":{"tags":[]},"execution_count":45}]},{"metadata":{"id":"kDtXECCHrDqM","colab_type":"code","outputId":"2d771c1d-d669-4c52-9535-b9c8d5042322","colab":{"base_uri":"https://localhost:8080/","height":575},"executionInfo":{"status":"ok","timestamp":1550300881644,"user_tz":-480,"elapsed":822,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["ypreds"],"execution_count":82,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([ 9.370206 , 23.062994 , 29.085371 , 13.11144  ,  9.3538475,\n","       22.259153 , 16.031366 , 15.605875 , 15.955183 , 15.79971  ,\n","       22.947327 , 35.25754  , 20.989542 , 29.958952 , 21.836102 ,\n","       11.539962 , 21.198475 , 24.981768 , 24.80271  , 23.974459 ,\n","       17.182869 , 16.271545 , 25.684208 , 21.692862 , 19.898388 ,\n","       16.186777 , 22.40797  , 25.815397 , 23.518883 , 17.929146 ,\n","       35.61698  , 20.399681 , 20.397526 , 24.013855 , 22.593496 ,\n","       24.140965 , 15.611827 , 23.991482 , 18.168013 , 35.157097 ,\n","       17.10456  , 20.556757 , 34.535564 , 18.753372 , 15.395048 ,\n","       29.785215 , 45.40446  , 19.041388 , 10.453054 , 37.60364  ,\n","       25.415462 , 20.995129 , 20.675165 , 46.56558  , 28.193459 ,\n","       26.096367 , 18.925188 , 20.633738 , 18.054153 , 19.032524 ,\n","       14.844367 , 22.616034 , 19.82583  , 31.420166 , 29.925508 ,\n","       19.926214 , 20.403868 , 16.70398  , 22.773792 , 17.528723 ,\n","       28.27874  , 41.765095 , 29.346258 , 23.660807 , 19.72792  ,\n","       23.510042 , 41.543865 , 27.08516  , 21.368006 , 20.296837 ,\n","       18.424213 , 45.821896 , 23.497196 , 10.319688 , 25.333155 ,\n","       23.071283 , 17.416462 , 21.431263 , 16.69718  , 13.599677 ,\n","       21.185175 , 19.004042 , 41.890106 , 32.27586  , 22.894089 ,\n","       12.083587 , 16.00302  , 20.135242 ,  9.636833 , 10.3434925,\n","       32.17203  , 17.201433 , 24.312597 , 23.764198 , 31.950375 ,\n","       42.38904  , 20.211275 ,  9.222834 , 23.529106 , 15.204091 ,\n","       45.714264 , 22.726345 , 21.93505  , 22.83943  , 18.831007 ,\n","       28.357115 , 23.656063 , 18.506319 , 44.399246 , 17.718763 ,\n","       23.581434 , 25.242235 , 15.822701 , 17.742668 , 15.002801 ,\n","       21.540045 , 31.707129 , 10.425569 , 20.334206 , 18.740664 ,\n","       15.563984 , 19.988941 ,  8.430399 , 28.774847 , 28.49874  ,\n","       20.645348 , 19.514614 , 21.691877 , 10.758142 , 17.69713  ,\n","       42.45072  , 17.867498 , 23.118143 , 19.08224  , 31.050879 ,\n","       19.580172 , 11.742813 , 37.957565 , 25.982744 , 21.890667 ,\n","       15.432159 , 28.850945 ], dtype=float32)"]},"metadata":{"tags":[]},"execution_count":82}]},{"metadata":{"id":"0G7z8VrWrDqR","colab_type":"code","outputId":"06d4dce6-bf27-40e2-9dbf-a4b4b12ccdb1","colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"status":"ok","timestamp":1550300885053,"user_tz":-480,"elapsed":789,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["MSE(Ytest, ypreds)"],"execution_count":83,"outputs":[{"output_type":"execute_result","data":{"text/plain":["9.130310409443075"]},"metadata":{"tags":[]},"execution_count":83}]},{"metadata":{"id":"8J_B9xUwrDqU","colab_type":"code","outputId":"d2b1448f-9478-4d4a-9e90-298450381253","colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"status":"ok","timestamp":1550300887328,"user_tz":-480,"elapsed":825,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["r2_score(Ytest,ypreds)"],"execution_count":84,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.9018814700174407"]},"metadata":{"tags":[]},"execution_count":84}]},{"metadata":{"id":"WbpwAhNIInlR","colab_type":"text"},"cell_type":"markdown","source":["## 3分类案例：XGB中的样本不均衡问题"]},{"metadata":{"id":"8QP5fKZ5rDqY","colab_type":"code","colab":{}},"cell_type":"code","source":["import numpy as np\n","import xgboost as xgb\n","import matplotlib.pyplot as plt\n","from xgboost import XGBClassifier as XGBC\n","from sklearn.datasets import make_blobs #自创数据集\n","from sklearn.model_selection import train_test_split as TTS\n","from sklearn.metrics import confusion_matrix as cm, recall_score as recall, roc_auc_score as auc"],"execution_count":0,"outputs":[]},{"metadata":{"id":"gy7MQKcCrDqa","colab_type":"code","colab":{}},"cell_type":"code","source":["class_1 = 500 #类别1有500个样本\n","class_2 = 50 #类别2只有50个\n","centers = [[0.0, 0.0], [2.0, 2.0]] #设定两个类别的中心\n","clusters_std = [1.5, 0.5] #设定两个类别的方差，通常来说，样本量比较大的类别会更加松散\n","X, y = make_blobs(n_samples=[class_1, class_2],\n","                  centers=centers,\n","                  cluster_std=clusters_std,\n","                  random_state=0, shuffle=False)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"3yUPohwIrDqc","colab_type":"code","outputId":"d8ab1ad0-fec6-40de-ea07-59260ad2b59d","colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"status":"ok","timestamp":1550300934731,"user_tz":-480,"elapsed":837,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["X.shape"],"execution_count":87,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(550, 2)"]},"metadata":{"tags":[]},"execution_count":87}]},{"metadata":{"id":"k4Sxp2g5rDqe","colab_type":"code","outputId":"9c2f7208-199e-4314-84f9-a84fdf8d9a0c","colab":{"base_uri":"https://localhost:8080/","height":467},"executionInfo":{"status":"ok","timestamp":1550300936792,"user_tz":-480,"elapsed":783,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["y"],"execution_count":88,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1,\n","       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n","       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])"]},"metadata":{"tags":[]},"execution_count":88}]},{"metadata":{"id":"7WTmg5W7rDqj","colab_type":"code","outputId":"72ad719d-2cb1-432e-d159-822dfd849e17","colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"status":"ok","timestamp":1550300939351,"user_tz":-480,"elapsed":803,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["(y == 1).sum() / y.shape[0] #9%"],"execution_count":89,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.09090909090909091"]},"metadata":{"tags":[]},"execution_count":89}]},{"metadata":{"id":"VvHBJCyYI4XU","colab_type":"text"},"cell_type":"markdown","source":["###  在数据集上建模：sklearn模式"]},{"metadata":{"id":"g_mhniQRrDqm","colab_type":"code","colab":{}},"cell_type":"code","source":["Xtrain, Xtest, Ytrain, Ytest = TTS(X,y,test_size=0.3,random_state=420)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"_xRqe7wFrDqn","colab_type":"code","colab":{}},"cell_type":"code","source":["#在sklearn下建模#\n","\n","clf = XGBC().fit(Xtrain,Ytrain)\n","ypred = clf.predict(Xtest)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"SEHUO9rBrDqr","colab_type":"code","outputId":"6051f76e-f207-4a76-8861-f5bde73e988a","colab":{"base_uri":"https://localhost:8080/","height":161},"executionInfo":{"status":"ok","timestamp":1550300980894,"user_tz":-480,"elapsed":809,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["ypred"],"execution_count":92,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n","       0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n","       1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n","       0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","       0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n","       0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1])"]},"metadata":{"tags":[]},"execution_count":92}]},{"metadata":{"id":"EU4VTlpwrDqt","colab_type":"code","outputId":"12d7d89e-f505-464e-e19f-43050730a2e3","colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"status":"ok","timestamp":1550300984179,"user_tz":-480,"elapsed":796,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["clf.score(Xtest,Ytest) #默认模型评估指标 - 准确率"],"execution_count":93,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.9272727272727272"]},"metadata":{"tags":[]},"execution_count":93}]},{"metadata":{"id":"wd_nIJjjrDqv","colab_type":"code","outputId":"4569c767-d6ae-4d1e-d5e1-c6e97d8de6be","colab":{"base_uri":"https://localhost:8080/","height":53},"executionInfo":{"status":"ok","timestamp":1550300989681,"user_tz":-480,"elapsed":780,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["cm(Ytest,ypred,labels=[1,0]) #少数类写在前面"],"execution_count":94,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[  9,   4],\n","       [  8, 144]])"]},"metadata":{"tags":[]},"execution_count":94}]},{"metadata":{"id":"ImVFs2LZrDqz","colab_type":"code","outputId":"809ed900-710b-4f7e-af76-7c8729fcab8e","colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"status":"ok","timestamp":1550300996783,"user_tz":-480,"elapsed":834,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["recall(Ytest,ypred)"],"execution_count":95,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.6923076923076923"]},"metadata":{"tags":[]},"execution_count":95}]},{"metadata":{"id":"iwl_T6OSrDq0","colab_type":"code","outputId":"eb9edb48-883a-44e2-ec10-09bd30d7a271","colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"status":"ok","timestamp":1550301000104,"user_tz":-480,"elapsed":805,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["auc(Ytest,clf.predict_proba(Xtest)[:,1])"],"execution_count":96,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.9671052631578947"]},"metadata":{"tags":[]},"execution_count":96}]},{"metadata":{"id":"br2AsiCTrDq2","colab_type":"code","colab":{}},"cell_type":"code","source":["#负/正样本比例\n","clf_ = XGBC(scale_pos_weight=10).fit(Xtrain,Ytrain)\n","ypred_ = clf_.predict(Xtest)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"iqom4PUgJJZ0","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":35},"outputId":"ecb1c002-c590-487c-f7a3-7dcafe8729ad","executionInfo":{"status":"ok","timestamp":1550301061375,"user_tz":-480,"elapsed":822,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["clf_.score(Xtest,Ytest)\n","\n"],"execution_count":100,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.9515151515151515"]},"metadata":{"tags":[]},"execution_count":100}]},{"metadata":{"id":"nwUl3VruJKKC","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":52},"outputId":"123f423b-2319-4cbb-c7d9-e55e5719a015","executionInfo":{"status":"ok","timestamp":1550301063312,"user_tz":-480,"elapsed":860,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["cm(Ytest,ypred_,labels=[1,0])"],"execution_count":101,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[ 13,   0],\n","       [  8, 144]])"]},"metadata":{"tags":[]},"execution_count":101}]},{"metadata":{"id":"2b4t__yvJMP0","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":35},"outputId":"aa8af668-0d7e-4045-d072-b2328d1ed51e","executionInfo":{"status":"ok","timestamp":1550301071714,"user_tz":-480,"elapsed":829,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["recall(Ytest,ypred_)"],"execution_count":102,"outputs":[{"output_type":"execute_result","data":{"text/plain":["1.0"]},"metadata":{"tags":[]},"execution_count":102}]},{"metadata":{"id":"TpR_6585JPl7","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":35},"outputId":"d661a0af-e195-498e-c79c-4ef33150d68c","executionInfo":{"status":"ok","timestamp":1550301073672,"user_tz":-480,"elapsed":796,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["auc(Ytest,clf_.predict_proba(Xtest)[:,1])"],"execution_count":103,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.9696356275303644"]},"metadata":{"tags":[]},"execution_count":103}]},{"metadata":{"id":"79Iu-ZStrDq3","colab_type":"code","outputId":"ed3f50e4-ac71-4441-a4d1-1f6ffb900a62","colab":{"base_uri":"https://localhost:8080/","height":363},"executionInfo":{"status":"ok","timestamp":1550301025382,"user_tz":-480,"elapsed":827,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["#随着样本权重逐渐增加，模型的recall,auc和准确率如何变化？\n","for i in [1,5,10,20,30]:\n","    clf_ = XGBC(scale_pos_weight=i).fit(Xtrain,Ytrain)\n","    ypred_ = clf_.predict(Xtest)\n","    print(i)\n","    print(\"\\tAccuracy:{}\".format(clf_.score(Xtest,Ytest)))\n","    print(\"\\tRecall:{}\".format(recall(Ytest,ypred_)))\n","    print(\"\\tAUC:{}\".format(auc(Ytest,clf_.predict_proba(Xtest)[:,1])))"],"execution_count":98,"outputs":[{"output_type":"stream","text":["1\n","\tAccuracy:0.9272727272727272\n","\tRecall:0.6923076923076923\n","\tAUC:0.9671052631578947\n","5\n","\tAccuracy:0.9454545454545454\n","\tRecall:0.9230769230769231\n","\tAUC:0.9665991902834008\n","10\n","\tAccuracy:0.9515151515151515\n","\tRecall:1.0\n","\tAUC:0.9696356275303644\n","20\n","\tAccuracy:0.9515151515151515\n","\tRecall:1.0\n","\tAUC:0.9706477732793523\n","30\n","\tAccuracy:0.9515151515151515\n","\tRecall:1.0\n","\tAUC:0.9701417004048584\n"],"name":"stdout"}]},{"metadata":{"id":"-zDjD6MNrDq5","colab_type":"code","outputId":"7d7a44a4-16d5-435e-ee6c-3a9496860395","colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"status":"ok","timestamp":1550301088867,"user_tz":-480,"elapsed":825,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["#负/正样本比例\n","clf_ = XGBC(scale_pos_weight=20).fit(Xtrain,Ytrain)\n","ypred_ = clf_.predict(Xtest)\n","clf_.score(Xtest,Ytest)"],"execution_count":104,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.9515151515151515"]},"metadata":{"tags":[]},"execution_count":104}]},{"metadata":{"id":"_1Sg7IYDrDq7","colab_type":"code","outputId":"cb78768f-ea34-4505-b384-b7cc0979fcc7","colab":{"base_uri":"https://localhost:8080/","height":52},"executionInfo":{"status":"ok","timestamp":1550301090434,"user_tz":-480,"elapsed":526,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["cm(Ytest,ypred_,labels=[1,0])"],"execution_count":105,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[ 13,   0],\n","       [  8, 144]])"]},"metadata":{"tags":[]},"execution_count":105}]},{"metadata":{"id":"8aQMGOy2rDq9","colab_type":"code","outputId":"e5c8728f-e8ca-4dd2-f40f-3b8c62484219","colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"status":"ok","timestamp":1550301093544,"user_tz":-480,"elapsed":790,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["recall(Ytest,ypred_)"],"execution_count":106,"outputs":[{"output_type":"execute_result","data":{"text/plain":["1.0"]},"metadata":{"tags":[]},"execution_count":106}]},{"metadata":{"id":"0yGLpGgFrDq-","colab_type":"code","outputId":"f0d2858a-d511-4f35-f42c-e9ab7bc92d38","colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"status":"ok","timestamp":1550301095022,"user_tz":-480,"elapsed":531,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["auc(Ytest,clf_.predict_proba(Xtest)[:,1])"],"execution_count":107,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.9706477732793523"]},"metadata":{"tags":[]},"execution_count":107}]},{"metadata":{"id":"9VYNhtarJXCo","colab_type":"text"},"cell_type":"markdown","source":["### 在数据集上建模：xgboost模式"]},{"metadata":{"id":"784D69eurDrA","colab_type":"code","colab":{}},"cell_type":"code","source":["dtrain = xgb.DMatrix(Xtrain,Ytrain)\n","dtest = xgb.DMatrix(Xtest,Ytest)\n","\n","#看看xgboost库自带的predict接口\n","param= {'silent':True,'objective':'binary:logistic',\"eta\":0.1,\"scale_pos_weight\":1}\n","num_round = 100"],"execution_count":0,"outputs":[]},{"metadata":{"id":"tEtLzlGerDrB","colab_type":"code","colab":{}},"cell_type":"code","source":["bst = xgb.train(param, dtrain, num_round)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"x2Zlfko1rDrE","colab_type":"code","colab":{}},"cell_type":"code","source":["preds = bst.predict(dtest)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"1nGdP0R7rDrG","colab_type":"code","outputId":"7aad8860-17a8-4d05-a263-85293e80b9b4","colab":{"base_uri":"https://localhost:8080/","height":605},"executionInfo":{"status":"ok","timestamp":1550301117274,"user_tz":-480,"elapsed":834,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["#看看preds返回了什么？\n","preds"],"execution_count":111,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0.00110357, 0.00761518, 0.00110357, 0.00110357, 0.93531454,\n","       0.00466839, 0.00110357, 0.00110357, 0.00110357, 0.00110357,\n","       0.00110357, 0.00410493, 0.00454478, 0.00571528, 0.00751026,\n","       0.00110357, 0.00110357, 0.00110357, 0.00110357, 0.00110357,\n","       0.00110357, 0.00110357, 0.00110357, 0.00110357, 0.00110357,\n","       0.00712637, 0.00110357, 0.00110357, 0.00110357, 0.00110357,\n","       0.00110357, 0.00110357, 0.00110357, 0.00793251, 0.00466839,\n","       0.00110357, 0.00339395, 0.00657186, 0.00110357, 0.00457053,\n","       0.00571528, 0.0026763 , 0.00110357, 0.00110357, 0.00110357,\n","       0.00884932, 0.00712637, 0.00110357, 0.00712637, 0.00466839,\n","       0.00110357, 0.00110357, 0.00712637, 0.00110357, 0.00110357,\n","       0.00110357, 0.00110357, 0.63748044, 0.00110357, 0.00793251,\n","       0.00110357, 0.00451971, 0.00644181, 0.00110357, 0.00110357,\n","       0.00110357, 0.00110357, 0.00751026, 0.00712637, 0.00110357,\n","       0.00866458, 0.00110357, 0.00110357, 0.00110357, 0.91610426,\n","       0.00110357, 0.00110357, 0.89246494, 0.0026763 , 0.00501714,\n","       0.00761518, 0.00884932, 0.00339395, 0.00110357, 0.93531454,\n","       0.00110357, 0.00110357, 0.00110357, 0.82530665, 0.00751026,\n","       0.00110357, 0.35174078, 0.00110357, 0.00110357, 0.70393246,\n","       0.00110357, 0.76804197, 0.00110357, 0.00110357, 0.00110357,\n","       0.00110357, 0.96656513, 0.00110357, 0.00571528, 0.25400913,\n","       0.00110357, 0.00110357, 0.00110357, 0.00110357, 0.00457053,\n","       0.00110357, 0.00110357, 0.00110357, 0.89246494, 0.00110357,\n","       0.9518535 , 0.0026763 , 0.00712637, 0.00110357, 0.00501714,\n","       0.00110357, 0.00110357, 0.00571528, 0.00110357, 0.00110357,\n","       0.00712637, 0.00110357, 0.00110357, 0.00712637, 0.00110357,\n","       0.25136763, 0.00110357, 0.00110357, 0.00110357, 0.00110357,\n","       0.00110357, 0.8904051 , 0.3876418 , 0.00110357, 0.00457053,\n","       0.00657186, 0.9366597 , 0.00866458, 0.00110357, 0.00501714,\n","       0.00501714, 0.00110357, 0.00110357, 0.00368543, 0.00501714,\n","       0.9830577 , 0.00110357, 0.00644181, 0.00110357, 0.00571528,\n","       0.00110357, 0.00110357, 0.00110357, 0.00110357, 0.00466839,\n","       0.00110357, 0.00110357, 0.92388713, 0.90231985, 0.80084217],\n","      dtype=float32)"]},"metadata":{"tags":[]},"execution_count":111}]},{"metadata":{"id":"hDWghDVurDrJ","colab_type":"code","colab":{}},"cell_type":"code","source":["#自己设定阈值\n","ypred = preds.copy()\n","ypred[preds > 0.5] = 1\n","ypred[preds != 1] = 0"],"execution_count":0,"outputs":[]},{"metadata":{"id":"2y8gcp50Jn2e","colab_type":"code","colab":{}},"cell_type":"code","source":["#自己设定阈值\n","ypred = preds.copy()\n","ypred[preds >= 0.5] = 1\n","ypred[preds < 0.5] = 0"],"execution_count":0,"outputs":[]},{"metadata":{"id":"F2sywDchrDrK","colab_type":"code","outputId":"fdc1f5bd-9555-4b42-ccdb-a2fee12c039e","colab":{"base_uri":"https://localhost:8080/","height":190},"executionInfo":{"status":"ok","timestamp":1550301183231,"user_tz":-480,"elapsed":798,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["ypred"],"execution_count":115,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n","       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n","       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n","       0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n","       0., 0., 0., 0., 0., 0., 1., 0., 0., 1., 0., 0., 0., 0., 0., 0., 1.,\n","       0., 0., 0., 1., 0., 0., 0., 0., 0., 1., 0., 1., 0., 0., 0., 0., 1.,\n","       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 1., 0., 0., 0.,\n","       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n","       1., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0.,\n","       0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1.], dtype=float32)"]},"metadata":{"tags":[]},"execution_count":115}]},{"metadata":{"id":"X--X5Cb8rDrM","colab_type":"code","colab":{}},"cell_type":"code","source":["#写明参数\n","scale_pos_weight = [1,5,10]\n","names = [\"negative vs positive: 1\"\n","         ,\"negative vs positive: 5\"\n","         ,\"negative vs positive: 10\"]"],"execution_count":0,"outputs":[]},{"metadata":{"id":"RC13_e3OrDrQ","colab_type":"code","outputId":"7717d55f-5711-4304-fdfc-52c3ab271b92","colab":{"base_uri":"https://localhost:8080/","height":225},"executionInfo":{"status":"ok","timestamp":1550301213111,"user_tz":-480,"elapsed":810,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["#导入模型评估指标\n","from sklearn.metrics import accuracy_score as accuracy, recall_score as recall, roc_auc_score as auc\n","\n","for name,i in zip(names,scale_pos_weight):\n","    param= {'silent':True,'objective':'binary:logistic'\n","            ,\"eta\":0.1,\"scale_pos_weight\":i}\n","    clf = xgb.train(param, dtrain, num_round)\n","    preds = clf.predict(dtest)\n","    ypred = preds.copy()\n","    ypred[preds > 0.5] = 1\n","    ypred[preds <=0.5] = 0\n","    print(name)\n","    print(\"\\tAccuracy:{}\".format(accuracy(Ytest,ypred)))\n","    print(\"\\tRecall:{}\".format(recall(Ytest,ypred)))\n","    print(\"\\tAUC:{}\".format(auc(Ytest,preds)))"],"execution_count":118,"outputs":[{"output_type":"stream","text":["negative vs positive: 1\n","\tAccuracy:0.9272727272727272\n","\tRecall:0.6923076923076923\n","\tAUC:0.9741902834008097\n","negative vs positive: 5\n","\tAccuracy:0.9393939393939394\n","\tRecall:0.8461538461538461\n","\tAUC:0.9635627530364372\n","negative vs positive: 10\n","\tAccuracy:0.9515151515151515\n","\tRecall:1.0\n","\tAUC:0.9665991902834008\n"],"name":"stdout"}]},{"metadata":{"id":"friRUXo9rDrY","colab_type":"code","outputId":"df4890b2-6290-49a7-b982-a0aeee3c40f1","colab":{"base_uri":"https://localhost:8080/","height":1054},"executionInfo":{"status":"ok","timestamp":1550301364520,"user_tz":-480,"elapsed":798,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["#你发现了什么现象？\n","\n","#难道是阈值的问题？让我们来挣扎一下\n","for name,i in zip(names,scale_pos_weight):\n","    for thres in [0.1,0.3,0.5,0.7,0.9]:\n","        param= {'silent':True,'objective':'binary:logistic'\n","                ,\"eta\":0.1,\"scale_pos_weight\":i}\n","        clf = xgb.train(param, dtrain, num_round)\n","        preds = clf.predict(dtest)\n","        ypred = preds.copy()\n","        ypred[preds > thres] = 1\n","        ypred[ypred != 1] = 0\n","        print(\"{},thresholds:{}\".format(name,thres))\n","        print(\"\\tAccuracy:{}\".format(accuracy(Ytest,ypred)))\n","        print(\"\\tRecall:{}\".format(recall(Ytest,ypred)))\n","        print(\"\\tAUC:{}\".format(auc(Ytest,preds)))"],"execution_count":119,"outputs":[{"output_type":"stream","text":["negative vs positive: 1,thresholds:0.1\n","\tAccuracy:0.9515151515151515\n","\tRecall:1.0\n","\tAUC:0.9741902834008097\n","negative vs positive: 1,thresholds:0.3\n","\tAccuracy:0.9393939393939394\n","\tRecall:0.8461538461538461\n","\tAUC:0.9741902834008097\n","negative vs positive: 1,thresholds:0.5\n","\tAccuracy:0.9272727272727272\n","\tRecall:0.6923076923076923\n","\tAUC:0.9741902834008097\n","negative vs positive: 1,thresholds:0.7\n","\tAccuracy:0.9212121212121213\n","\tRecall:0.6153846153846154\n","\tAUC:0.9741902834008097\n","negative vs positive: 1,thresholds:0.9\n","\tAccuracy:0.9515151515151515\n","\tRecall:0.5384615384615384\n","\tAUC:0.9741902834008097\n","negative vs positive: 5,thresholds:0.1\n","\tAccuracy:0.9515151515151515\n","\tRecall:1.0\n","\tAUC:0.9635627530364372\n","negative vs positive: 5,thresholds:0.3\n","\tAccuracy:0.9515151515151515\n","\tRecall:1.0\n","\tAUC:0.9635627530364372\n","negative vs positive: 5,thresholds:0.5\n","\tAccuracy:0.9393939393939394\n","\tRecall:0.8461538461538461\n","\tAUC:0.9635627530364372\n","negative vs positive: 5,thresholds:0.7\n","\tAccuracy:0.9272727272727272\n","\tRecall:0.6923076923076923\n","\tAUC:0.9635627530364372\n","negative vs positive: 5,thresholds:0.9\n","\tAccuracy:0.9212121212121213\n","\tRecall:0.6153846153846154\n","\tAUC:0.9635627530364372\n","negative vs positive: 10,thresholds:0.1\n","\tAccuracy:0.9515151515151515\n","\tRecall:1.0\n","\tAUC:0.9665991902834008\n","negative vs positive: 10,thresholds:0.3\n","\tAccuracy:0.9515151515151515\n","\tRecall:1.0\n","\tAUC:0.9665991902834008\n","negative vs positive: 10,thresholds:0.5\n","\tAccuracy:0.9515151515151515\n","\tRecall:1.0\n","\tAUC:0.9665991902834008\n","negative vs positive: 10,thresholds:0.7\n","\tAccuracy:0.9393939393939394\n","\tRecall:0.8461538461538461\n","\tAUC:0.9665991902834008\n","negative vs positive: 10,thresholds:0.9\n","\tAccuracy:0.9212121212121213\n","\tRecall:0.6153846153846154\n","\tAUC:0.9665991902834008\n"],"name":"stdout"}]},{"metadata":{"id":"Ajn4CcOoMjqN","colab_type":"text"},"cell_type":"markdown","source":["### xgboost模式探索-ys"]},{"metadata":{"id":"Y1a9DOkrrDrb","colab_type":"code","colab":{}},"cell_type":"code","source":["param= {'silent':True,'objective':'binary:logistic',\"eta\":0.1,\"scale_pos_weight\":1}\n","num_round = 100\n","bst = xgb.train(param, dtrain, num_round)\n","preds = bst.predict(dtest)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"OaBlaA6CM2oa","colab_type":"code","colab":{}},"cell_type":"code","source":["ypred = preds.copy()\n","ypred[preds > 0.5] = 1\n","ypred[ypred != 1] = 0"],"execution_count":0,"outputs":[]},{"metadata":{"id":"OwXJ1jEpM71r","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":190},"outputId":"8d4fd10f-a35c-4ec9-b4ae-422761860b1a","executionInfo":{"status":"ok","timestamp":1550302047721,"user_tz":-480,"elapsed":609,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["ypred"],"execution_count":122,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n","       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n","       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n","       0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n","       0., 0., 0., 0., 0., 0., 1., 0., 0., 1., 0., 0., 0., 0., 0., 0., 1.,\n","       0., 0., 0., 1., 0., 0., 0., 0., 0., 1., 0., 1., 0., 0., 0., 0., 1.,\n","       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 1., 0., 0., 0.,\n","       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n","       1., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0.,\n","       0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1.], dtype=float32)"]},"metadata":{"tags":[]},"execution_count":122}]},{"metadata":{"id":"2oJTAuE6M-qN","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":52},"outputId":"f649c113-d98d-4b2f-b09f-80870e724b61","executionInfo":{"status":"ok","timestamp":1550302147289,"user_tz":-480,"elapsed":816,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["cm(Ytest,ypred,labels=[1,0])"],"execution_count":125,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[  9,   4],\n","       [  8, 144]])"]},"metadata":{"tags":[]},"execution_count":125}]},{"metadata":{"id":"sXOCRCHCND9a","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":35},"outputId":"2c2471bc-7392-4f4e-98e2-726d50108d01","executionInfo":{"status":"ok","timestamp":1550302153112,"user_tz":-480,"elapsed":776,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["accuracy(Ytest,ypred)"],"execution_count":126,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.9272727272727272"]},"metadata":{"tags":[]},"execution_count":126}]},{"metadata":{"id":"Jh2AM6mxNP4o","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":35},"outputId":"4868b29f-e9ba-4069-e013-cb8b0a4f27c1","executionInfo":{"status":"ok","timestamp":1550302188577,"user_tz":-480,"elapsed":745,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["recall(Ytest,ypred)"],"execution_count":127,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.6923076923076923"]},"metadata":{"tags":[]},"execution_count":127}]},{"metadata":{"id":"C84qZ6NBNynZ","colab_type":"code","colab":{}},"cell_type":"code","source":[""],"execution_count":0,"outputs":[]},{"metadata":{"id":"iW5giXNUNhAf","colab_type":"code","colab":{}},"cell_type":"code","source":["param10= {'silent':True,'objective':'binary:logistic',\"eta\":0.1,\"scale_pos_weight\":10}\n","num_round = 100\n","bst = xgb.train(param10, dtrain, num_round)\n","preds = bst.predict(dtest)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"zlxVnkgRN4No","colab_type":"code","colab":{}},"cell_type":"code","source":["ypred = preds.copy()\n","ypred[preds > 0.5] = 1\n","ypred[ypred != 1] = 0"],"execution_count":0,"outputs":[]},{"metadata":{"id":"ht1R7ln7N7md","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":52},"outputId":"3c59e2d3-e3e3-4ccc-c459-cee0803c841b","executionInfo":{"status":"ok","timestamp":1550302309918,"user_tz":-480,"elapsed":718,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["cm(Ytest,ypred,labels=[1,0])"],"execution_count":130,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[ 13,   0],\n","       [  8, 144]])"]},"metadata":{"tags":[]},"execution_count":130}]},{"metadata":{"id":"zgW7yO-8N-pQ","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":35},"outputId":"580db188-2983-4ff1-b407-ec3a40e51e82","executionInfo":{"status":"ok","timestamp":1550302321536,"user_tz":-480,"elapsed":755,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["accuracy(Ytest,ypred)"],"execution_count":131,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.9515151515151515"]},"metadata":{"tags":[]},"execution_count":131}]},{"metadata":{"id":"El5waFNNOBd3","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":35},"outputId":"e2db4a23-95a6-4d31-af10-816a8e95df5b","executionInfo":{"status":"ok","timestamp":1550302331153,"user_tz":-480,"elapsed":765,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"cell_type":"code","source":["recall(Ytest,ypred)"],"execution_count":132,"outputs":[{"output_type":"execute_result","data":{"text/plain":["1.0"]},"metadata":{"tags":[]},"execution_count":132}]},{"metadata":{"id":"NOVjrNoWOD0Z","colab_type":"code","colab":{}},"cell_type":"code","source":[""],"execution_count":0,"outputs":[]}]}